A Fast Point Cloud Ground Segmentation Approach Based on Coarse-To-Fine Markov Random Field

Huang, Weixin; Liang, Huawei; Lin, Linglong; Wang, Zhong Lin; Wang, Shaobo; Yu, Biao; Niu, Runxin

doi:10.1109/tits.2021.3073151

Cited by 41 publications

(15 citation statements)

References 29 publications

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“…The two vehicles are tested in a regular urban environment, and the vehicle's trajectory is designed to be on a right turn road section. This paper uses ground segmentation with the original point cloud [15] and then performs clustering processing [16] in the process of point cloud data. Moreover, we use the object tracking algorithm [41] to evaluate the pose estimation algorithm's effect on tracking results.…”

Section: Experimental Results Of Our Experimental Platformmentioning

confidence: 99%

“…In contrast, the adaptability of traditional methods is much better. In the traditional object detection pipeline of the 3D point cloud, it is generally necessary to first perform ground segmentation with the original point cloud [15] and then perform clustering processing with the point cloud data [16]. The object detection task is finally completed after estimating the pose of each obstacle according to the clustering results [17].…”

Section: Of 30mentioning

confidence: 99%

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Optimal Vehicle Pose Estimation Network Based on Time Series and Spatial Tightness with 3D LiDARs

et al. 2021

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Vehicle pose estimation is essential in autonomous vehicle (AV) perception technology. However, due to the different density distributions of the point cloud, it is challenging to achieve sensitive direction extraction based on 3D LiDAR by using the existing pose estimation methods. In this paper, an optimal vehicle pose estimation network based on time series and spatial tightness (TS-OVPE) is proposed. This network uses five pose estimation algorithms proposed as candidate solutions to select each obstacle vehicle's optimal pose estimation result. Among these pose estimation algorithms, we first propose the Basic Line algorithm, which uses the road direction as the prior knowledge. Secondly, we propose improving principal component analysis based on point cloud distribution to conduct rotating principal component analysis (RPCA) and diagonal principal component analysis (DPCA) algorithms. Finally, we propose two global algorithms independent of the prior direction. We provided four evaluation indexes to transform each algorithm into a unified dimension. These evaluation indexes’ results were input into the ensemble learning network to obtain the optimal pose estimation results from the five proposed algorithms. The spatial dimension evaluation indexes reflected the tightness of the bounding box and the time dimension evaluation index reflected the coherence of the direction estimation. Since the network was indirectly trained through the evaluation index, it could be directly used on untrained LiDAR and showed a good pose estimation performance. Our approach was verified on the SemanticKITTI dataset and our urban environment dataset. Compared with the two mainstream algorithms, the polygon intersection over union (P-IoU) average increased by about 5.25% and 9.67%, the average heading error decreased by about 29.49% and 44.11%, and the average speed direction error decreased by about 3.85% and 46.70%. The experiment results showed that the ensemble learning network could effectively select the optimal pose estimation from the five abovementioned algorithms, making pose estimation more accurate.

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Section: Experimental Results Of Our Experimental Platformmentioning

confidence: 99%

Section: Of 30mentioning

confidence: 99%

Optimal Vehicle Pose Estimation Network Based on Time Series and Spatial Tightness with 3D LiDARs

et al. 2021

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“…Huang et al [ 119 ] propose an algorithm that aims at solving the high computational requirements of MRF-based methods. This algorithm starts by performing a coarse segmentation based on a ring-based elevation map where data points are arranged in rings whose diameters are proportional to the distance to the LiDAR sensor.…”

Section: Ground Segmentation Methodsmentioning

confidence: 99%

A Survey on Ground Segmentation Methods for Automotive LiDAR Sensors

Gomes

Matias

Campos

et al. 2023

Sensors

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In the near future, autonomous vehicles with full self-driving features will populate our public roads. However, fully autonomous cars will require robust perception systems to safely navigate the environment, which includes cameras, RADAR devices, and Light Detection and Ranging (LiDAR) sensors. LiDAR is currently a key sensor for the future of autonomous driving since it can read the vehicle’s vicinity and provide a real-time 3D visualization of the surroundings through a point cloud representation. These features can assist the autonomous vehicle in several tasks, such as object identification and obstacle avoidance, accurate speed and distance measurements, road navigation, and more. However, it is crucial to detect the ground plane and road limits to safely navigate the environment, which requires extracting information from the point cloud to accurately detect common road boundaries. This article presents a survey of existing methods used to detect and extract ground points from LiDAR point clouds. It summarizes the already extensive literature and proposes a comprehensive taxonomy to help understand the current ground segmentation methods that can be used in automotive LiDAR sensors.

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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%

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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A Fast Point Cloud Ground Segmentation Approach Based on Coarse-To-Fine Markov Random Field

Cited by 41 publications

References 29 publications

Optimal Vehicle Pose Estimation Network Based on Time Series and Spatial Tightness with 3D LiDARs

Optimal Vehicle Pose Estimation Network Based on Time Series and Spatial Tightness with 3D LiDARs

A Survey on Ground Segmentation Methods for Automotive LiDAR Sensors

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

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