lidar inertial odometry

For full functionality of this site, please enable JavaScript. Assuming that the labels of all points are n or m, the minimum value of the corresponding probability of each point is calculated as a weighted term. This paper presents a method taking into account the remaining motion uncertainties of the trajectory used to de-skew a point cloud along with the environment geometry to increase the robustness of current registration algorithms. Most existing SLAM systems dont give much consideration to this issue, and most of them assume that the environment is static. Second, those methods do not make full use of the normals, which only take the point clouds as the inputs. Liu, F.Xie, Y.Yang, Q.Wei, and Q.Fei, Ds-slam: A awa We present the DLR Planetary Stereo, Solid-State LiDAR, Inertial (S3LI) 5135-5142. The matching-based odometry has great limitations. [8] proposed a place recognition method using a combination of BoW and point features based on the relative pose estimation, which is a method of applying local descriptors to a SLAM framework. ACPR 2021. Our system consists of four modules: LiDAR inertial odometry (LIO), LiDAR global matching (LGM), pose graph based fusion (PGF) and environmental change detection (ECD). Our \end{aligned}$$, $$\begin{aligned} \begin{aligned}&i=\sigma (W_{i}x+b_i), \\&g=tanh(W_{g}x+b_g), \\&o=\sigma (W_{o}x+b_o), \\&out=o*tanh(i*g) . If you are using other lidar sensors, you may need to change the name of this time channel and make sure that it is the relative time in a scan. First, a pair of siamese LSTMs are used to obtain the initial pose from the linear acceleration and angular velocity of IMU. change detection from mobile laser scanning point clouds,, B.Bescos, J.M. Fcil, J.Civera, and J.Neira, Dynaslam: Tracking, Visual odometry is used in a variety of applications, such as mobile robots, self-driving cars, and unmanned aerial vehicles. Fig. which was referenced to a base station to evaluate the accuracy of the mapping results. Hence we use the pose error between IMU preintegration and scan-matching to generate the initial resolution dynamically. FAST-LIO2 achieves consistently higher accuracy at a much lower computation load than other state-of-the-art LiDAR-inertial navigation systems, and ikd-Tree, an incremental k-d tree data structure that enables incremental updates and dynamic rebalancing, achieves superior overall performance while naturally supports downsampling on the tree. The authors ranked 1st on the competitive SemanticKITTI [17] leaderboard upon publication. If one pixel coordinate has no matching 3D points, the pixel value is set to (0,0,0). 4(c), the prediction resolution is almost consistent with the true resolution, and the final resolution is always bigger than the true resolution. As shown in Fig. PLOS is a nonprofit 501(c)(3) corporation, #C2354500, based in San Francisco, California, US. stream The length S of IMU sequence is 15. Finally, we conduct ablation studies to verify the effectiveness of the innovative part of our model. Note that the sequence 00 and 03 in Table 3 have no results. This paper presents a tightly-coupled multi-sensor fusion algorithm termed LiDAR-inertial-camera fusion (LIC-Fusion), which efficiently fuses IMU measurements, sparse visual features, and extracted LiDAR points . 4), our method without imu assist is also competitive compared to Cho et al. Future work includes compressing network parameters to improve computational efficiency and designing a rotation invariant global descriptor based on the current loop closure detection work. our method can outperform [5, 15]. The proposed approach is evaluated on the KITTI odometry estimation benchmark and achieves comparable performances against other state-of-the-art methods. Comparison of feature points and constraint relationships between frames: (a) edge feature points and loss le, (b) planar feature points and loss lp. So we can conclude that removal-first is another effective method to improve SLAM performance in dynamic environments. Given the predicted relative pose $T_{k,k+1}$, we apply it on preprocessed current point clouds $DP_{k+1}$ and its normals $NP_{k+1}$. We compare the original method with the two strategies of not using normal vectors as the network input and not distinguishing feature of the normals and points (see Fig. 3). Unlike Cho et al. The point clouds generated by the lidar can provide high-precision 3D measurements, but if it has large translation or orientation in a short time, the continuously generated point clouds will only get few matching points, which will affect the accuracy of odometry. })l96 h6Jg2iv)4~0[f|;s{c:-J[)UUE&)NlUC(,iK|/"z5L[rr@ZqG_aSM-i%tw&V}G1O|nSPA4^D=\!&?#%":IfNn6G5P&#T*/d{uNz.nvm6Za>B_Pr].p](Vj=_T{mws{Rdrm|=3n^_) The result of sequence 05 is slightly worse than LIO-SAM-ODOM. Then, the feature extraction module compensates the motion distortion of the point cloud [16]. The results are further sent to the graph factor module to get a globally consistent pose estimate. We denoted as extracted features combining semantic and geometric information at time k, where is the set of all semantic planar features and is the set of all semantic edge features. range images and uses tightly-coupled lidar inertial odometry to first remove We see that the runtime of RF-LIO (FA) is less than 100 ms in low dynamic environments, while in high dynamic environments, it is also less than 121 ms. The first is a fast keyframe-based LiDAR scan-matcher that . Output: To balance the real-time performance and removal rate, and to avoid the influence caused by the wrong prediction resolution, we set a minimum resolution r0 and appropriately enlarge the predicted resolution r. The final initial resolution rf is defined as follows: Where is a coefficient greater than 1. r0=vertical FOVvertical rays, which can effectively avoid empty pixel values in range image. Although removal-after also removes the moving points, the scan-matching has been completed at this time. Attention. Therefore, most state-of-the-art SLAM approaches that initially designed in static environments cannot handle these severe dynamic scenes [8]. Histogram FilterGlobal Matching LiDAR Global Matching(LGM) Prebuilt Localization Map Sliding Window Optimization Pose Graph Fusion(PGF . We note that both RF-LIO and LIO-SAM use the same feature extraction and loop closure detection methods, and both use GTSAM[24] to optimize the factor graph. By clicking accept or continuing to use the site, you agree to the terms outlined in our. cartography,, C.Chen and B.Yang, Dynamic occlusion detection and inpainting of in situ Therefore, some extracted local descriptors are not suitable for outdoor loop closure detection. [16] developed their own 3D Neural Architecture Search (3D-NAS) designed to outperform previous methods with a large margin. In: Robotics: Science and Systems, vol. They can be classified into supervised methods and unsupervised methods. LOAM does not perform well on all three datasets. The adjustment size is 0.01m per time. 3d map maintenance in dynamic environments, in, M.Kaess, H.Johannsson, R.Roberts, V.Ila, J.J. Leonard, and F.Dellaert, In: CoRL (2020), Zhang, J., Singh, S.: LOAM: LiDAR odometry and mapping in real-time. We extend the method with semantic information to improve the accuracy and efficiency of feature matching. We adopt step scheduler with a step size of 20 and $\gamma = 0.5$ to control the training procedure, the initial learning rate is $10^{-4}$ and the batch size is 20. Autonomous vehicles usually collect information by perceiving the surrounding environment in real time and use on-board sensors such as lidar, Inertial Measurement Units (IMU), or camera to estimate their 3D translation and orientation. The details and final point cloud map from RF-LIO are shown in Fig. xc```b``c`f``1e`0D"(f`eg`#6.ff[ !} USGS national map imagery and mapping results of LIO-CSI. The objects in dynamic scenarios also cause the failure of place recognition because these dynamic objects may not be in their original positions when the vehicle returns to the same place. x t = [ 0.2, 0.1, ( 11 p i) / 60] (Where the vectors are [x, y, theta] with respect to some world frame.) 2D estimated trajectories of our method on sequence 09 and 10. Fig. Some screenshots of the test environment are shown in Fig. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, Peoples Republic of China, Affiliations: In this case, we let Ppiror be the point cloud of current observation. Hence RF-LIO can be applied to various scenes robustly. R2DIO:Real-time, RGB-colored, Depth-Inertial Indoor Odometry for RGB-D cameras - GitHub - jiejie567/R2DIO: R2DIO:Real-time, RGB-colored, Depth-Inertial Indoor Odometry for RGB-D cameras . The evaluation results on the JLU campus dataset demonstrate that our proposed LIO-CSI method has a good generalization ability and robustness. To achieve the moving objects removal-first without an accurate pose, there are several key steps: (i) The initial pose is obtained by IMU odometry. Hence, these methods fall into a chicken-and-egg problem: moving object points removal relies on accurate pose, while accurate pose can not be obtained when the point cloud map contains moving objects. The proposed network is implemented in PyTorch [16] and trained with a single NVIDIA Titan RTX GPU. \end{aligned}$$, $$\begin{aligned} \mathcal {L}=\alpha \mathcal {L}_{po2pl}+\lambda \mathcal {L}_{po2pl}, \end{aligned}$$, $$\begin{aligned} out=tanh(W_2(tanh(W_{1}x+b_1))+b_2). For validation, we use self-collected datasets and open UrbanLoco datasets [7], where the Urbanloco datasets contain a large number of moving objects. Extrinsic Calibration, Challenges of SLAM in extremely unstructured environments: the DLR UnDeepLIO: Unsupervised Deep Lidar-Inertial Odometry, $\Pi :{\mathbb {R}}^3\mapsto {\mathbb {R}}^2$, $$\begin{aligned} \begin{aligned} \left( \begin{array}{c} w \\ h \\ \end{array} \right) =&\left( \begin{array}{c} (f_w-\arctan (\frac{p_y}{p_x}))/\eta _w \\ (f_h-\arcsin (\frac{p_z}{d}))/\eta _h\\ \end{array} \right) , \\&H> h \ge 0, W > w \ge 0, \end{aligned} \end{aligned}$$, $$\begin{aligned} \boldsymbol{n}_p=\sum _{i\in [0,3]}w_{p_i,p}(v_{p_i}-v_p)\times w_{p_{i+1},p}(v_{p_{i+1}}-v_p), \end{aligned}$$, $$\begin{aligned} T_{k,k+1}^{4\times 4}= \left[ \begin{array}{cc} R_{k,k+1}^{3\times 3} &{} t_{k,k+1}^{3\times 1} \\ 0 &{} 1 \end{array} \right] , \end{aligned}$$, $$\begin{aligned} \boldsymbol{v}_{k+1,p}^{k}=R_{k,k+1}\boldsymbol{v}_{k+1,p}^{k+1}+t_{k,k+1}, \end{aligned}$$, $$\begin{aligned} \boldsymbol{n}_{k+1,p}^{k}=R_{k,k+1}\boldsymbol{n}_{k+1,p}^{k+1}. VILENS: Visual, Inertial, Lidar, and Leg Odometry for All-Terrain Legged Robots. from 3-d point cloud data by traversing a voxel occupancy grid,, R.Ambru, N.Bore, J.Folkesson, and P.Jensfelt, Meta-rooms: Building Although we can obtain a class label for each point predicted by the SPVNAS network, the results of semantic segmentation still suffer from noise and annotation errors, which lead to low confidence and misclassification. The accumulated error is eliminated by a semantic-assisted loop closure detection method. It segments and reconstructs raw input sweeps from spinning LiDAR to obtain reconstructed sweeps with higher frequency. Building on a highly efficient tightly coupled iterated Kalman filter, FAST-LIO2 has two key novelties that allow fast, robust, and accurate LiDAR navigation (and mapping). In the hard task, comparing to the most advanced method SelfVoxeLO [22] which uses 3D convolutions on voxels and consumes much video memory and training time, our method also can get comparable results with IMU. If there is a loop closure, a new constraint will be added into the optimization process to eliminate the error caused by the cumulative calculation between frames. Finally. Combining multiple LiDARs enables a robot to maximize its perceptual Relative pose error of LIO-SAM and LIO-SAM-ODOM. $f_h$ depends on the type of the lidar. Moreover, a global descriptor combined with semantic information is proposed to improve the performance of loop closure detection. In this experiment, when the feature points fall on the moving object, the LOAM map shown in Fig. First, we use sequences 00-08 for training and 09-10 for testing, as Cho et al. RTLIO: Real-Time LiDAR-Inertial Odometry and Mapping for UAVs Authors Jung-Cheng Yang 1 , Chun-Jung Lin 1 , Bing-Yuan You 1 , Yin-Long Yan 1 , Teng-Hu Cheng 1 Affiliation 1 Department of Mechanical Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan. Then, the label correction module uses a clustering strategy to correct the results sent by the SPVNAS network. In: Wallraven, C., Liu, Q., Nagahara, H. (eds) Pattern Recognition. RF-LIO, to solve the SLAM problem in high dynamic environments. 2021 IEEE International Conference on Robotics and Automation (ICRA). 2: Overall framework of RF-LIO. In Table 4, we find that LIO-SAM-ODOM has a significant improvement in average mRPE results compared to LIO-SAM. ROS (tested with indigo, kinetic, and melodic) gtsam (Georgia Tech Smoothing and Mapping library, 4.0.0-alpha2) Each vertex/normal pair consists of last and current frames. Besides, compared with using all scan points, the sparsity of edge points can ensure that the method is robust enough. From the features, we propose an attention layer (by formula (6), x is input) which is inspired by LSTM [9] to estimate residual pose $\delta T$ between last frame and the remapped current frame. The feature extraction process mentioned in LOAM, or LIO-SAM rely on geometric information to assess the environment and use these geometric features to establish association between scans. Mapping the Point Clouds of Current Frame to the Last Frame. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Specifically, we exploit the label information provided by the deep semantic segmentation network to handle the dynamic elements. IEEE (2012), He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. The initial side length of voxel downsample is set to 0.3m, it is adjusted according to the number of points after downsample, if points are too many, we increase the side length, otherwise reduce. t means translation and q means Euler angle of orientation. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Peoples Republic of China, the rest of UAVs, all in real-time and only based on LiDAR-inertial mea- surements. It is also evenly divided by an angle, which is called a sector. Note: this is the main journal version of our work. From the results, it can be seen that RF-LIO (After) and LIO-SAM have similar performance, while RF-LIO (First) and RF-LIO (FA), using removal-first, have a significant improvement. School of Computer Science and Engineering, South China University of Technology, Guangzhou, Peoples Republic of China. We count the number of residual moving points in the maps of LIO-SAM and RF-LIO, respectively, and then calculate the removal rate. >"@UWf s^W%`X&=?Zv4~,wsz2`7cyaW+4w[1x"lP{Rg[d9lUIvX,.Q@7I1l@]^[rJC2j~) They encode the geometric relationship between points into a histogram or matrix to calculate the similarity between frames [6, 7, 9]. The average mean relative translational error of our proposed method is 2.6975%. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Peoples Republic of China, Affiliations: 13 (2004), Nubert, J., Khattak, S., Hutter, M.: Self-supervised learning of lidar odometry for robotic applications. Model-based methods are: LOAM [23] and LeGO-LOAM [19]. The accuracy of registration is improved by integrating semantic and geometric information. Rotating Lidar Inertial Odometry and Mapping. In a more realistic environment, semantic information can filter out dynamic objects in the data, such as pedestrians and vehicles, which lead to information redundancy in generated map and map-based localization failure. U6gq8w0nx.l7{;qSiK,:EF VUw>44wwms`[Y-!7vkan For example, LOAM, proposes a feature extraction strategy based on edge and plane, which has become the most widely used method for low-drift and real-time state estimation and mapping. These methods are based on the principle that when there is a closer point in a narrow FOV, the farther point will be occluded. 2, no. One possible explanation is that the number of similar geometric features increases with the increase of distance, but constraints can be established through assisted semantic information to filter some similar geometric features and reduce the occurrence of false matching. x;r8 PubMedGoogle Scholar. The estimated motion from inertial measurement unit (IMU) pre-integration de-skews point clouds and produces an initial guess . broad scope, and wide readership a perfect fit for your research every time. Fig 4 shows the extracted edge and planar feature points as well as the built constraint relationship between frames. The GPS data is only used as the ground truth. $\eta _w$ and $\eta _h$ control the horizontal and vertical sampling density. Our method is effective in most sequences, especially in long-distance sequences such as 01, 02, 05, and 08. Therefore, this experiment is designed to prove the correctness of the method proposed in Sec. Voxel-based approaches: These methods are based on voxel ray casting [10, 11, 12]. Input: Hence, we added semantic information to the scan context and propose a new global descriptor. III-E. We evaluate our RF-LIO via a series of experiments and compare it with LOAM [1] and LIO-SAM [2]. Then, comparing to the existing lidar odometry methods, our model can obtain competitive results. For unsupervised training, we introduce an unsupervised loss function which is employed on the voxelized point clouds. Iii Removal-First Lidar Inertial Odometry Iii-a System Overview Fig. Saihang Gao, Their calculation process is generally more complicated and runs at a lower frequency. 9(8), 17351780 (1997), Javanmard-Gh, A., Iwaszczuk, D., Roth, S.: DeepLIO: deep LIDAR inertial sensor fusion for odometry estimation. The scan-context method [9] maps the 3D point cloud into a matrix through a bin encoding function and improves its efficiency using a two-step search strategy. Since the factor added to the graph is calculated by two consecutive keyframes, the above selection strategy not only ensures the balance between computational expense and map density, but it also ensures that the factor graph is sparse. But RF-LIO needs to remove the dynamic points before scan-matching (i.e. Supervised methods appear relatively early, Lo-net [12] maps the point clouds to 2D image by spherical projection. The removal-first refers to that the proposed RF-LIO first removes moving object without an accurate pose and then employ scan-matching. )`m"x&nq%Uw1W!HXtO7&7 *'KQ4C+S7i{OWE^&/)lXH ^Nr??`u#wISn,!_ fzOQ+}Iz*A2 >qM,2 OwI() D6A@q3!re g[H B6lp%|H!I,hMi)f|70v We name it as Ours-easy. Instead, we use the tightly-coupled inertial measurement unit (IMU) odometry to obtain a rough initial state estimation. 2, no. [15] imitate ICP algorithm, using the nearest neighbor as the matching point(see Fig. The proposed adaptive range image moving points removal algorithm does not rely on any prior training data, nor is it limited by the category and number of moving objects. 4, no. Gauss-Newton iteration methods have a long-standing history in odometry task. Robust and accurate localization is the premise for an intelligent vehicle or mobile robot. Then we send the remapped maps into ResNet Encoder, which outputs feature maps, including vertex and normal features. In this paper, we proposed UnDeepLIO, an unsupervised learning-based odometry network. ResNet [8] is used in image recognition. Normal Map. LiDAR Inertial Odometry Aided Robust LiDAR Localization System in Changing City Scenes Wendong Ding, Shenhua Hou, Hang Gao, Guowei Wan, Shiyu Song1 Abstract Environmental fluctuations pose crucial challenges to a localization system in autonomous driving. Three GNSS antennas are placed around the roof rack to form two baselines along the lateral and longitudinal directions to obtain the pose information of the vehicle. However, Removert uses fixed resolutions, as it is based on accurate localization information. From the features, we propose an attention layer to estimate residual pose $\delta T$. Citation: Wang G, Gao S, Ding H, Zhang H, Cai H (2021) LIO-CSI: LiDAR inertial odometry with loop closure combined with semantic information. Trajectory prediction with graph-based dual-scale context fusion. (ii) RF-LIO use this initial resolution to construct the range image from the current lidar scan and the corresponding submap separately. stream It is summarized in Alg. Direct LiDAR-Inertial Odometry: Lightweight LIO with Continuous-Time Motion Correction Kenny Chen, Ryan Nemiroff, Brett T. Lopez Aggressive motions from agile flights or traversing irregular terrain induce motion distortion in LiDAR scans that can degrade state estimation and mapping. Calculating the relative position between frames requires huge computing resources in order to use every LiDAR frame for calculating and adding the results to the factor graph. From the comparison between the generated map and the available USGS National Map imagery, we can see that they are highly consistent. We strongly recommend the users read this document thoroughly and test the package with the provided dataset first. L. Zhang, P. Li, J. Chen and S. Shen. The existing LVIO multi-sensor fusion strategy can be described from the front-end and back-end perspectives. (2022). Moreover, a registration based on a geometric feature is vulnerable to the interference of a dynamic object, resulting in a decline of accuracy. Therefore, this structure can extract feature in our task as well. If it is convergent, after graph optimization, the final fine resolution is used to remove the remaining moving points in the current keyframe. In: CVPR, pp. preintegration theory for fast and accurate visual-inertial navigation,, E.Palazzolo and C.Stachniss, Fast image-based geometric change detection Therefore, we only use the feature of the point to estimate the translation. ISPRS 1, 4754 (2021), Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. Through the calculation formula of the normal vector, we can know that the change of the normal vector is only related to the orientation, and the translation will not bring about the change of the normal vector. We denote the edge and plane features from the keyframe k+1 as Fek+1 and Fpk+1, respectively. State estimation and mapping are fundamental parts of an autonomous driving system, which are also the kernel idea of simultaneous localization and mapping (SLAM). Then, the similarity score of these semantic-assisted scan-context images can be calculated, and the frame corresponding to the image with the highest score is detected as the loop closure frame. It is inevitable that some dynamic objects are recorded into the data during the data collection process, such as pedestrians, vehicles, etc. Simultaneous Localization and Mapping (SLAM) is considered to be an essential To show the benefits of removing moving points before scan-matching, we divide RF-LIO into three different types. We optimize the parameters with the Adam optimizer [11] whose hyperparameter values are $\beta _1 = 0.9$, $\beta _2 = 0.99$ and $w_{decay} = 10^{-5}$. first obtain the accurate poses of multiple measurements by scan-matching and then iteratively update the state (i.e. In order to circumvent the disordered nature of point clouds, we project the point clouds into the 2D image coordinate system according to the horizontal and vertical angle. Typically, ICP [1] and its variants [14, 18] iteratively find matching points which depend on nearest-neighbor searching and optimize the translation and orientation by matching points. (6) static or dynamic) of a map voxel by fusing the semantic information. In: NIPS (2019), Pauly, M.: Point primitives for interactive modeling and processing of 3D geometry. The relative rotational result of sequence 09 is slightly higher than the comparison method, but the relative translation error is greatly reduced. The loss weight of formula (11) is set to be $\alpha = 1.0$ and $\lambda = 0.1$. In this paper, we used the SPVNAS network to provide semantics for the point cloud of each scan. The combination of features helps to obtain better results, especially in dynamic scenarios. Our Direct LiDAR-Inertial Odometry (DLIO) algorithm utilizes a hybrid architecture that combines the benefits of loosely-coupled and tightly-coupled IMU integration to enhance reliability and real-time performance while improving accuracy. J.Behley, C.Stachniss, and F.Fraunhofer, Overlapnet: Loop closing for Fast 3D sparse topological skeleton graph generation for mobile robot global planning. However, most of these methods are designed for 3D model registration, rather than place recognition. The proposed method develops a new synthetic structure of neural networks for implementing the Lidar-inertial odometry to generate a 6 degree of freedom pose estimation. This paper proposes a new LiDAR-inertial odometry framework that generates accurate state estimates and detailed maps in real-time on resource-constrained mobile robots. In my head . Thanks to the development of deep semantic segmentation network, we can conveniently obtain semantic information from point clouds. 7177. In: IROS (2019), Xu, Y., et al. If only use IMU to extract features through the network, and directly merge with the feature of the point clouds, the effect is limited (see Fig. where the points with ci are smaller than threshold and represent the semantic planar features; moreover, the points with ci, which are larger than , are semantic edge features. Velas et al. To improve the distinguishability of global descriptors, it is necessary to introduce the semantic information into the descriptor coding process. A liDAR-inertial odometry with principled uncertainty modeling. The state of the system can be represented as follows: where RSO(3) is the rotation matrix, pR3, Where ^B and ^aB are the raw IMU measurements in B and are affected by the bias b. PLoS ONE 16(12): Then, we proposed a semantic assisted scan-context method to improve the accuracy and robustness of loop closure detection. R3LIVE is built upon their previous work R2LIVE, which contains two sub-systems: the LiDAR-inertial odometry (LIO) and the visual-inertial odometry (VIO). Lidar-inertial odometry This work was supported by Shanghai Automotive Industry Science and Technology Development Foundation (No. The radial direction is evenly divided by distance, which is called a ring. First, point cloud frames are encoded into semantic-assisted scan-context images. Then we transform them from Bk+1 to W and obtain In this test, the removal rate of moving points is used to evaluate RF-LIO. When scan-matching is performed, the translation and orientation errors of IMU preintegration can be computed as follows: Through (7), (8), (9), and (10), we can get the pose error of the previous keyframe k. However, before scan-matching, the IMU preintegration error of the current keyframe k+1 can not be obtained by using the above method. with LOAM and LIO-SAM, the absolute trajectory accuracy of the proposed RF-LIO [5] first apply unsupervised approach on deep-learning-based LiDAR odometry which is an extension of their previous approach [4]. localization in urban scenes, in, C.Yu, Z.Liu, X.-J. track_ odometry : synchronize Odometry and IMU Drop ROS Indigo and Ubuntu Trusty support Fix include directory priority Contributors: Atsushi Watanabe; 0.4.0 (2019-05-09). The loss function $\mathcal {L}_{pl2pl}$ is represented as, Overall Loss. The ablation experiment with LIO-SAM shows the effect of removal-first in RF-LIO. 4(b), we can see that the predicted orientation error is in good agreement with the true error. PMID: 34201217 PMCID: PMC8226800 DOI: 10.3390/s21123955 Then the edge and plane features are extracted for each new lidar scan by evaluating the roughness of points over a local area. Typically, light detection and ranging (LiDAR)-based SLAM methods collect real-time data from various sensors, such as LiDAR, inertial measurement units (IMUs), global navigation satellite system (GNSSs), etc., to calculate the trajectory of the current vehicles and complete the mapping task. By combining semantic information, we propose a semantic assisted scan-context method and replaced it with the loop closure detection strategy of LIO-SAM, which can correct drift errors and improve the performance of mapping results. Similarly to point-to-plane ICP, we use normal $\overline{np}_{k+1}$ of every point in $\overline{NP}_{k+1}$, corresponding normal vector of ${np}_k$ in ${NP}_{k}$ to compute the angle between a pair of matching plane. The iterative closest point (ICP) method [20] proposed by Besl et al. MULLS is an efficient, low-drift, and versatile 3D LiDAR SLAM system that ranks among the top LiDar-only SLAM systems with real-time performance on the KITTI benchmark. To reduce the amount of computation, we adopt the strategy of keyframe selection. In addition, these local descriptors are not discriminative enough to distinguish highly similar scenes. ACM 24(6), 381395 (1981), CrossRef Two issues exist in these methods. For this purpose, we collected three datasets with multiple moving objects in different environments. Although LeGO-LOAM [3] does not adopt the strategy of deep learning, it introduces label information into the odometry by the clustering method. Since LOAM and LIO-SAM are designed in a static environment, we compare with them to show the performance of RF-LIO in general cases. Shan T, Englot B, Meyers D, et al. With the initial pose, we perform the rigid transform on the current frame and align it to the last frame. : PyTorch: an imperative style, high-performance deep learning library. Each 3D point $\boldsymbol{p}=(p_x,p_y,p_z)$ in a point clouds $P_k$ is mapped into the 2D image plane (w,h) represented as. Inspired by image features, semantic information is combined with geometric information for registration to improve the accuracy of scan matching. In all experiments, RF-LIO uses the same parameters shown in Table I. In this experiment, we use three self-collected datasets with only a few moving objects. A real-time lidar-inertial odometry package. -Ue oxcoL*]? Sequence 09 has a total length of 1.7 kilometers and contains only one loop closure whose start point coincides with the end point. In this subsection, we introduce the experimental sensors and platform used in our experiments. : Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. (7) When the point clouds are sparse or the consecutive frames do not scan the same position of the same object, there will be a large deviation in pose estimation. The closer the objects are to the point with low confidence, the greater the reference value of its label. This work was supported by Shanghai Automotive Industry Science and Technology Development Foundation (No. First, the segmentation module, referred to as the SPVNAS network, takes a single scans points and predicts the semantic label for each point. 2 shows an overall framework of RF-LIO, which consists of three major modules: IMU preintegration, feature extraction, and mapping. [5] and Nubert et al. localisation and mapping, in, C.Forster, L.Carlone, F.Dellaert, and D.Scaramuzza, On-manifold We compared the proposed LIO-CSI method with two purely geometric information methods based on tightly-coupled LiDAR inertial odometry, LeGO-LOAM and the baseline method LIO-SAM. Particularly, 1) The Kitti dataset is available at http://www.cvlibs.net/datasets/kitti/raw_data.php. To further improve the localization performance, LiDAR-Visual-Inertial Odometry, as a multi-sensor fusion localization method, has become a research focus of SLAM with its advantages of multi-sensor heterogeneity and complementarity. Discover a faster, simpler path to publishing in a high-quality journal. [5] who use pixel locations as correspondence between two point clouds, we search correspondence on the whole point clouds. Pi,j is the coordinate information corresponding to block (i,j). However, humanity always uses semantic information to complete scene recognition in life. FC represents fully connected layer. Point registration is the most common skill for two-frame methods, where ICP [1] and its variants [14, 18] are typical examples. Fig. When. 10, pp. In addition, we note that the runtime of RF-LIO (First) is significantly less than RF-LIO (After), and even the runtime of RF-LIO (FA) is also less than RF-LIO (After). Then the error between IMU preintegration and scan-matching is used to determine an initial resolution (i.e. The authors would like to thank the laboratory members for their contribution to this paper. The attention module can self-learn the importance of features, and give higher weight to more important features. Table 4 shows the mRPE results of LIO-SAM and LIO-SAM-ODOM. Moving objects such as pedestrians and vehicles widely exist in real-world scenes. otherwise, a new resolution will be generated, and steps (ii), (iii), and (iv) will be repeated. In the easy task (For trajectories results, see Fig. We also propose a point-to-surfel (PTS) association scheme, continuous-time optimization on PTS and IMU preintegration factors, along with loop closure and bundle adjustment, making a complete framework for Lidar-Inertial continuous-time odometry and mapping. 47584765. LIMO suers from translation and rotation errors even more than existing algorithms such as Lidar Odometry and Mapping . Then, we use sequences 00-06 for training and 07-10 for testing, to compare with SelfVoxeLO which uses Sequences 00-06 as training set. Table 5 clearly shows that the use of semantic-assisted loop closure detection can eliminate the accumulated error of the odometry. More specifically, it consists of Velodynes HDL-32E surround LiDAR sensor, a 3DM-GX5 inertial measurement unit (IMU) sensor and a single board Trimble BD982 GNSS receiver module. 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