Efficient LiDAR Odometry for Autonomous Driving

Zheng, Xin Yu; Zhu, Jianke

doi:10.1109/lra.2021.3110372

Cited by 44 publications

(33 citation statements)

References 19 publications

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“…Gao and Tedrake [11] present a probabilistic model, which computes filter-based correspondences in E step and updates pose by Gauss-Newton algorithm in M step. In this letter, we employ filter registration to tackle the SSLs odometry problem, in which the permutohedral filter is replaced by an efficient patch filter on range image [5].…”

Section: Point Set Registrationmentioning

confidence: 99%

“…In contrast to KD-Tree [1], [2] and voxel [21], the range image is not only a low memory consumption but also computational efficient data structural. We extend this representation from conventional multi-line spinning LiDARs [5] to SSLs. A range image is an index table I : R 2 → R 3 that reserves the spatial relationship of 3D point set within single 2D image.…”

Section: B Filter Registration For Lidar Odometrymentioning

confidence: 99%

“…4, we only maintain single range image I map in global memory to retain the history map points. In EM procedure, there are two temporal image I v and I n for efficient data association, which are pre-computed by normal estimation [5]. The memory consumption of map points is dependent on the size of range image.…”

Section: Mappingmentioning

confidence: 99%

“…L IGHT detection and ranging (LiDAR) sensors can directly obtain the accurate range measurements in various scenarios by actively emitting the laser beams, which enables them to be the essential sensors for perception and navigation. Currently, the dominant LiDAR odometry approaches [1], [2], [3], [4], [5] make use of the multi-line mechanical spinning scanners due to their simplicity and success in many robotic applications.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, we directly register raw points from LiDAR to avoid the complicated feature extraction for the diverse scanning patterns. As in [5], we maintain the map points through a range image so that the spatial relationship of points can be employed to efficiently speed up the scan registration. Fig 2 shows the example reconstruction results of our proposed approach.…”

Section: Introductionmentioning

confidence: 99%

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Effective Solid State LiDAR Odometry Using Continuous-time Filter Registration

Zheng¹,

Zhu²

2022

Preprint

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Solid-state LiDARs are more compact and cheaper than the conventional mechanical multi-line spinning LiDARs, which have become increasingly popular in autonomous driving recently. However, there are several challenges for these new LiDAR sensors, including severe motion distortions, small field of view and sparse point cloud, which hinder them from being widely used in LiDAR odometry. To tackle these problems, we present an effective continuous-time LiDAR odometry (ECTLO) method for the Risley prism-based LiDARs with non-repetitive scanning patterns. To account for the noisy data, a filter-based point-to-plane Gaussian Mixture Model is used for robust registration. Moreover, a LiDAR-only continuous-time motion model is employed to relieve the inevitable distortions. To facilitate the implicit data association in parallel, we maintain all map points within a single range image. Extensive experiments have been conducted on various testbeds using the solid-state LiDARs with different scanning patterns, whose promising results demonstrate the efficacy of our proposed approach.

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Section: Point Set Registrationmentioning

confidence: 99%

Section: B Filter Registration For Lidar Odometrymentioning

confidence: 99%

Section: Mappingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effective Solid State LiDAR Odometry Using Continuous-time Filter Registration

Zheng¹,

Zhu²

2022

Preprint

Self Cite

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A Real‐Time and Fast LiDAR–IMU–GNSS SLAM System with Point Cloud Semantic Graph Descriptor Loop‐Closure Detection

Tian

Liu

et al. 2023

Advanced Intelligent Systems

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Herein, a real‐time, fast, tightly coupled simultaneous localization and mapping (SLAM) system is proposed. The deep neural network is used to segment the point cloud semantically to construct the point cloud semantic map descriptor, and the global navigation satellite system real‐time kinematic position is used to detect the loop closure. Finally, a factor‐graph model is used for global optimization. The working principle of the SLAM system is introduced in detail, including the construction of the factor graph, the generation of the point cloud semantic graph descriptor, the generation of the ring graph, the loop‐closure detection process, and the global optimization. Indoor and outdoor experiments are conducted to validate the performance of overall trajectory estimation and loop‐closure detection. Compared to traditional methods, the proposed method offers improved accuracy and real‐time performance in trajectory estimation. It effectively addresses issues such as limited feature constraints, large computational consumption, and subpar real‐time performance, allowing for quick and accurate loop‐closure detection. Moreover, the method demonstrates a time consumption reduction of two‐thirds compared to traditional approaches while exhibiting superior overall loop‐closure detection performance.

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LiDAR‐based object detection for agricultural robots

Stronzek‐Pfeifer,

Chemmadan,

Patel

et al. 2023

Proc Appl Math and Mech

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Light detection and ranging (LiDAR) sensors have proven to be a valuable tool to gather spatial information about the environment and are a crucial component in perception of autonomous systems. In the agricultural domain, state‐of‐the‐art algorithms for detection, classification, and tracking often utilize a combination of LiDAR and camera by fusing both semantic and spatial information. This is in part due to the availability of fast algorithms specifically optimized to make use of the convenient 2D data structure of RGB images. Still, there are limitations relying on cameras in agriculture because they highly depend on favorable lighting conditions and generally lack explicit geometric information, whereas LiDAR is especially advantageous in that regard as well as regarding range. This makes LiDAR particularly valuable for perception applications such as self‐localization, mapping, or object detection. The unstructured nature of 3D LiDAR point clouds, however, coupled with their possibly large size and density, presents a significant hurdle in terms of real‐time capability for these kinds of tasks. Object detection on 3D LiDAR sensor data is therefore challenging and solutions in agriculture usually are tied to very narrow use cases or rigorous down sampling to ensure real‐time applicability. Here, we present an algorithm featuring 2.5D map representations to avoid the computational drawbacks and ensure real‐time capability. We utilize established algorithms to project the 3D LiDAR data into two distinct maps and then apply object detection algorithms on each of those maps individually and to subsequently combine the information into a joint estimate. From the information stored in the 2.5D map, axis‐aligned bounding boxes for each object are computed containing information about position and dimensions. We present a proof of concept and assess the real‐time capability for a domain‐specific use case.

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Efficient LiDAR Odometry for Autonomous Driving

Cited by 44 publications

References 19 publications

Effective Solid State LiDAR Odometry Using Continuous-time Filter Registration

Effective Solid State LiDAR Odometry Using Continuous-time Filter Registration

A Real‐Time and Fast LiDAR–IMU–GNSS SLAM System with Point Cloud Semantic Graph Descriptor Loop‐Closure Detection

LiDAR‐based object detection for agricultural robots

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