A Probability Occupancy Grid Based Approach for Real-Time LiDAR Ground Segmentation

Luo, Zhongzhen; Mohrenschildt, Martin von; Habibi, Saeid

doi:10.1109/tits.2019.2900548

Cited by 28 publications

(11 citation statements)

References 26 publications

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“…The line-based methods mainly consider the scanning characteristics of LiDAR. In this method, the ground is divided into different segments according to the preset angle, and then each segment is divided into different small bins according to the distance [19][20][21][22][23][24][25][26][27][28][29][30]. By judging the spatial features or other features of the points in each bin, the reference ground height of each bin is obtained to establish the distinction between ground points and nonground points.…”

Section: Related Workmentioning

confidence: 99%

“…Compared with the previous GP-INSAC algorithm to fit the entire ground plane, the Gaussian process is simplified to one dimension and is only used to fit the ground reference line within a segment [20]. Luo et al calculated the probability that each bin belongs to the ground, and proposed a ground segmentation algorithm based on the occupancy probability grid, which has a good segmentation effect on various types of road surfaces [22]. P. Narks et al designed a segmented fitting strategy, which has better adaptability to slope conditions [23].…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, we evaluate the performance of ground segmentation using the ability resist over-segmentation and under-segmentation. Currently, there is no labeled open-source dataset which is used to evaluate the ground segmentation effect; many papers rely on visual effects to compare the effects of algorithms [15,22]. Therefore, the ground segmentation effect in this paper will be reflected by the ratio of ground points to all points in a frame of data.…”

Section: The Verification Effect Of the Algorithm On The Kitti Datasetmentioning

confidence: 99%

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A Robust Gaussian Process-Based LiDAR Ground Segmentation Algorithm for Autonomous Driving

et al. 2022

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Robust and precise vehicle detection is the prerequisite for decision-making and motion planning in autonomous driving. Vehicle detection algorithms follow three steps: ground segmentation, obstacle clustering and bounding box fitting. The ground segmentation result directly affects the input of the subsequent obstacle clustering algorithms. Aiming at the problems of over-segmentation and under-segmentation in traditional ground segmentation algorithms, a ground segmentation algorithm based on Gaussian process is proposed in this paper. To ensure accurate search of real ground candidate points as training data for Gaussian process, the proposed algorithm introduces the height and slope criteria, which is more reasonable than the use of fixed height threshold for searching. After that, a sparse covariance function is introduced as the kernel function for calculation in Gaussian process. This function is more suitable for ground segmentation situation the radial basis function (RBF). The proposed algorithm is tested on our autonomous driving experimental platform and the public autonomous driving dataset KITTI, compared with the most used RANSAC algorithm and ray ground filter algorithm. Experiment results show that the proposed algorithm can avoid obvious over-segmentation and under-segmentation. In addition, compared with the RBF, the introduction of the sparse covariance function also reduces the computation time by 37.26%.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: The Verification Effect Of the Algorithm On The Kitti Datasetmentioning

confidence: 99%

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A Robust Gaussian Process-Based LiDAR Ground Segmentation Algorithm for Autonomous Driving

et al. 2022

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show abstract

“…However, this method increases the computational burden which cannot meet real-time requirements. In [19], a probability occupancy grid-based ground segmentation method is proposed which can run online in different traffic scenarios. Shan et al [20] projected point cloud onto a range image then extracted ground points by calculating the neighborhood relationship between adjacent scan lines.…”

Section: Related Workmentioning

confidence: 99%

Integrate Point-Cloud Segmentation with 3D LiDAR Scan-Matching for Mobile Robot Localization and Mapping

et al. 2019

Sensors

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Localization and mapping are key requirements for autonomous mobile systems to perform navigation and interaction tasks. Iterative Closest Point (ICP) is widely applied for LiDAR scan-matching in the robotic community. In addition, the standard ICP algorithm only considers geometric information when iteratively searching for the nearest point. However, ICP individually cannot achieve accurate point-cloud registration performance in challenging environments such as dynamic environments and highways. Moreover, the computation of searching for the closest points is an expensive step in the ICP algorithm, which is limited to meet real-time requirements, especially when dealing with large-scale point-cloud data. In this paper, we propose a segment-based scan-matching framework for six degree-of-freedom pose estimation and mapping. The LiDAR generates a large number of ground points when scanning, but many of these points are useless and increase the burden of subsequent processing. To address this problem, we first apply an image-based ground-point extraction method to filter out noise and ground points. The point cloud after removing the ground points is then segmented into disjoint sets. After this step, a standard point-to-point ICP is applied into to calculate the six degree-of-freedom transformation between consecutive scans. Furthermore, once closed loops are detected in the environment, a 6D graph-optimization algorithm for global relaxation (6D simultaneous localization and mapping (SLAM)) is employed. Experiments based on publicly available KITTI datasets show that our method requires less runtime while at the same time achieves higher pose estimation accuracy compared with the standard ICP method and its variants.

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“…In addition, the real-time performance of data processing in LiDAR sensor is also an important issue that must be resolved. For example, Luo et al [25] proposed a real-time ground segmentation method based on probability occupancy grids. When there is occlusion, the LiDAR sensor is used to solve the environmental perception task, thereby reducing the processing scale of data and reducing the calculation time.…”

Section: Introductionmentioning

confidence: 99%

An Improved Camshift Tracking Algorithm Based on LiDAR Sensor

Zhu²

2021

Journal of Sensors

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Aiming at the problems of inaccurate interaction point position, interaction point drift, and interaction feedback delay in the process of LiDAR sensor signal processing interactive system, a target tracking algorithm is proposed by combining LiDAR depth image information with color images. The algorithm first fuses the gesture detection results of the LiDAR and the visual image and uses the color information fusion algorithm of the Camshift algorithm to realize the tracking of the moving target. The experimental results show that the multi-information fusion tracking algorithm based on this paper has achieved higher recognition rate and better stability and robustness than the traditional fusion tracking algorithm.

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A Probability Occupancy Grid Based Approach for Real-Time LiDAR Ground Segmentation

Cited by 28 publications

References 26 publications

A Robust Gaussian Process-Based LiDAR Ground Segmentation Algorithm for Autonomous Driving

A Robust Gaussian Process-Based LiDAR Ground Segmentation Algorithm for Autonomous Driving

Integrate Point-Cloud Segmentation with 3D LiDAR Scan-Matching for Mobile Robot Localization and Mapping

An Improved Camshift Tracking Algorithm Based on LiDAR Sensor

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