A Survey on Ground Segmentation Methods for Automotive LiDAR Sensors

Gomes, Tiago; Matias, Diogo; Campos, Ana M. F. Oliveira; Cunha, Luís; Roriz, Ricardo

doi:10.3390/s23020601

Cited by 19 publications

(10 citation statements)

References 140 publications

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“…LiDAR sensors can assist in detecting the road and the drivable area, where high-level algorithms are able to accurately identify road boundaries, markings, lanes, and curbs, aiding in a correct evaluation of the road and ensuring efficient navigation of the vehicle [17][18][19]. To better perform these tasks, a ground segmentation step can be applied to the point cloud data [20], which enhances the subsequent identification of environmental features.…”

Section: Drivable Area Detectionmentioning

confidence: 99%

A Survey on Data Compression Techniques for Automotive LiDAR Point Clouds

Roriz,

Silva,

Dias

et al. 2024

Sensors

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In the evolving landscape of autonomous driving technology, Light Detection and Ranging (LiDAR) sensors have emerged as a pivotal instrument for enhancing environmental perception. They can offer precise, high-resolution, real-time 3D representations around a vehicle, and the ability for long-range measurements under low-light conditions. However, these advantages come at the cost of the large volume of data generated by the sensor, leading to several challenges in transmission, processing, and storage operations, which can be currently mitigated by employing data compression techniques to the point cloud. This article presents a survey of existing methods used to compress point cloud data for automotive LiDAR sensors. It presents a comprehensive taxonomy that categorizes these approaches into four main groups, comparing and discussing them across several important metrics.

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Section: Drivable Area Detectionmentioning

confidence: 99%

A Survey on Data Compression Techniques for Automotive LiDAR Point Clouds

Roriz,

Silva,

Dias

et al. 2024

Sensors

Self Cite

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“…An optional pre-processing step is the de-trending of depth values, which is similar to ground filtering procedures (Silva et al 2018;Gomes et al 2023): For each 3D point, the mean depth of the k nearest neighbours is determined using the k-nearest-neighbours search algorithm of the library Open3D, based on FLANN (Muja et al, 2014; the experimental setups mentioned in Section 4.2 used k=200). The actual depth of the points is reduced by this mean depth.…”

Section: Point Cloud-based Boulder Detectionmentioning

confidence: 99%

“…Ground filtering of 3D point data is a widely used procedure in machine learning applications for automotive LiDAR data (Gomes et al, 2023), 3D object detection (Wang et al, 2022), and digital terrain modelling (Silva et al, 2018), which could not be fully explored in this study. As a disadvantage, the technique adds an additional processing step before model training and detection, which could be time consuming.…”

Section: S-te1mentioning

confidence: 99%

AI-based boulder detection in sonar data – bridging the gap from experimentation to application

Hinz,

Westfeld,

Feldens

et al. 2024

IHR

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The detection of boulders in hydroacoustic data is essential for a range of environmental, economic and marine planning applications. The manual interpretation of hydroacoustic data for object detection is a non-trivial, tedious and subjective task. Using the conventional means accessible to hydrographic professionals, it is nearly impossible to locate all boulders or rule out their presence for extended areas of interest. Although it has been shown that AI can do the job quickly and reproducibly, earlier work has not progressed beyond scientific experiments. As a result, AI software have not been routinely integrated into the workflows of institutions involved in hydrographic data acquisition and processing, or oceanographic analysis. This paper presents a workflow for fully automated boulder detection in hydroacoustic data. A graphical user interface enables training and evaluation of detection models, boulder detection model execution, and post-processing of detection results without programming. The workflow is demonstrated on data from the southern Baltic Sea. Validation results of the detection for various data inputs include a mAP-50 of 77.83 % for raster images of backscatter intensities based on side-scan sonar, a mAP-50 of 70.46 % for raster images of slope angles based on multibeam echosounder and a mAP-50 of 44.02 % for backscatter and bathymetric data given as 3D point clouds.

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“…As a preprocessing step, ground segmentation algorithms play a crucial role in filtering out irrelevant information for subsequent perception tasks [9]. By dividing the 3D point cloud into ground and non-ground points, this algorithm can effectively reduce data volume and computational requirements.…”

Section: Introductionmentioning

confidence: 99%

A Staged Real-Time Ground Segmentation Algorithm of 3D LiDAR Point Cloud

Deng,

Chen,

Jiang

2024

Electronics

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Ground segmentation is a crucial task in the field of 3D LiDAR perception for autonomous driving. It is commonly used as a preprocessing step for tasks such as object detection and road extraction. However, the existing ground segmentation algorithms often struggle to meet the requirements of robustness and real-time performance due to significant variations in ground slopes and flatness across different scenes, as well as the influence of objects such as grass, flowerbeds, and trees in the environment. To address these challenges, this paper proposes a staged real-time ground segmentation algorithm. The proposed algorithm not only achieves high real-time performance but also exhibits improved robustness. Based on a concentric zone model, the algorithm filters out reflected noise points and vertical non-ground points in the first stage, improving the validity of the fitted ground plane. In the second stage, the algorithm effectively addresses the issue of undersegmentation of ground points through three steps: ground plane fitting, ground plane validity judgment, and ground plane repair. The experimental results on the SemanticKITTI dataset demonstrate that the proposed algorithm outperforms the existing methods in terms of segmentation results.

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A Survey on Ground Segmentation Methods for Automotive LiDAR Sensors

Cited by 19 publications

References 140 publications

A Survey on Data Compression Techniques for Automotive LiDAR Point Clouds

A Survey on Data Compression Techniques for Automotive LiDAR Point Clouds

AI-based boulder detection in sonar data – bridging the gap from experimentation to application

A Staged Real-Time Ground Segmentation Algorithm of 3D LiDAR Point Cloud

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