A Novel Lidar Inertial Odometry with Moving Object Detection for Dynamic Scenes

Hu, Xiao; Li, Yan; Xie, Hong; Dai, Junhu; Zhao, Yinghao; Su, Shan

doi:10.1109/icus55513.2022.9986661

Cited by 3 publications

(3 citation statements)

References 27 publications

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“…Based on the preprocessed LiDAR point cloud, ground segmentation, point cloud cluster clustering, and point cloud cluster geometric center coordinate calculations are conducted using the method provided in Reference [ 41 ] to construct the point cloud cluster center point set corresponding to each frame of the LiDAR point cloud. The point cloud cluster center point set corresponding to the frame point cloud is recorded as

.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, two open-source segmentation-based methods, including a traditional clustering segmentation method and a learning segmentation method, are selected as comparison methods. Specifically, the LiDAR dynamic point cloud detection method based on calculating the similarity scores of the corresponding clusters in adjacent frames described in Reference [ 41 ] is selected as comparison method 1, and the deep-learning-based LiDAR dynamic point cloud detection method described in Reference [ 14 ] is selected as comparison method 2. Based on the data collected in three test scenes, the LiDAR dynamic target detection method based on multidimensional features proposed in this paper is comprehensively evaluated by comparing the detection accuracy and detection efficiency of these methods.…”

Section: Experiments and Analysismentioning

confidence: 99%

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LiDAR Dynamic Target Detection Based on Multidimensional Features

Xu,

Gao,

Sui

et al. 2024

Sensors

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To address the limitations of LiDAR dynamic target detection methods, which often require heuristic thresholding, indirect computational assistance, supplementary sensor data, or postdetection, we propose an innovative method based on multidimensional features. Using the differences between the positions and geometric structures of point cloud clusters scanned by the same target in adjacent frame point clouds, the motion states of the point cloud clusters are comprehensively evaluated. To enable the automatic precision pairing of point cloud clusters from adjacent frames of the same target, a double registration algorithm is proposed for point cloud cluster centroids. The iterative closest point (ICP) algorithm is employed for approximate interframe pose estimation during coarse registration. The random sample consensus (RANSAC) and four-parameter transformation algorithms are employed to obtain precise interframe pose relations during fine registration. These processes standardize the coordinate systems of adjacent point clouds and facilitate the association of point cloud clusters from the same target. Based on the paired point cloud cluster, a classification feature system is used to construct the XGBoost decision tree. To enhance the XGBoost training efficiency, a Spearman’s rank correlation coefficient-bidirectional search for a dimensionality reduction algorithm is proposed to expedite the optimal classification feature subset construction. After preliminary outcomes are generated by XGBoost, a double Boyer–Moore voting-sliding window algorithm is proposed to refine the final LiDAR dynamic target detection accuracy. To validate the efficacy and efficiency of our method in LiDAR dynamic target detection, an experimental platform is established. Real-world data are collected and pertinent experiments are designed. The experimental results illustrate the soundness of our method. The LiDAR dynamic target correct detection rate is 92.41%, the static target error detection rate is 1.43%, and the detection efficiency is 0.0299 s. Our method exhibits notable advantages over open-source comparative methods, achieving highly efficient and precise LiDAR dynamic target detection.

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.…”

Section: Methodsmentioning

confidence: 99%

Section: Experiments and Analysismentioning

confidence: 99%

LiDAR Dynamic Target Detection Based on Multidimensional Features

Xu,

Gao,

Sui

et al. 2024

Sensors

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show abstract

“…In addition, RF-LIO employs graph optimization to enhance pose estimation further. Similar with RF-LIO, Hu et al [57] leverage segmentation-based moving object detection and verification into FAST-LIO2 [152] to handle inaccurate data association in dynamic environments. LIMOT [178] estimates poses of ego vehicle and dynamic target objects with trajectory-based multi-object tracking.…”

Section: Tightly Coupled Approachesmentioning

confidence: 99%

LiDAR odometry survey: recent advancements and remaining challenges

Lee,

Jung,

Yang

et al. 2024

Intel Serv Robotics

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Odometry is crucial for robot navigation, particularly in situations where global positioning methods like global positioning system are unavailable. The main goal of odometry is to predict the robot’s motion and accurately determine its current location. Various sensors, such as wheel encoder, inertial measurement unit (IMU), camera, radar, and Light Detection and Ranging (LiDAR), are used for odometry in robotics. LiDAR, in particular, has gained attention for its ability to provide rich three-dimensional (3D) data and immunity to light variations. This survey aims to examine advancements in LiDAR odometry thoroughly. We start by exploring LiDAR technology and then scrutinize LiDAR odometry works, categorizing them based on their sensor integration approaches. These approaches include methods relying solely on LiDAR, those combining LiDAR with IMU, strategies involving multiple LiDARs, and methods fusing LiDAR with other sensor modalities. In conclusion, we address existing challenges and outline potential future directions in LiDAR odometry. Additionally, we analyze public datasets and evaluation methods for LiDAR odometry. To our knowledge, this survey is the first comprehensive exploration of LiDAR odometry.

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RDP-LOAM: Remove-Dynamic-Points LiDAR Odometry and Mapping

Cao,

Wei,

et al. 2023

2023 IEEE International Conference on Unmanned Systems (ICUS)

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A Novel Lidar Inertial Odometry with Moving Object Detection for Dynamic Scenes

Cited by 3 publications

References 27 publications

LiDAR Dynamic Target Detection Based on Multidimensional Features

LiDAR Dynamic Target Detection Based on Multidimensional Features

LiDAR odometry survey: recent advancements and remaining challenges

RDP-LOAM: Remove-Dynamic-Points LiDAR Odometry and Mapping

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