Novel Laser-Based Obstacle Detection for Autonomous Robots on Unstructured Terrain

Chen, Wei; Liu, Qianjie; Hu, Huosheng; Li, Jun; Wang, Shaojie; Zhu, Qingyuan

doi:10.3390/s20185048

Cited by 11 publications

(2 citation statements)

References 37 publications

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“…A laser-based system with the Sober algorithm and the Gaussian kernel function has been applied by Chen et al [ 41 ]. The goal is to group each obstacle’s point clouds, so the super-voxel has been optimized with the Euclidean clustering technique.…”

Section: Positive Obstacles Detection and Analysismentioning

confidence: 99%

Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review

Islam¹,

Nabi²,

Ball³

2022

Sensors

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When it comes to some essential abilities of autonomous ground vehicles (AGV), detection is one of them. In order to safely navigate through any known or unknown environment, AGV must be able to detect important elements on the path. Detection is applicable both on-road and off-road, but they are much different in each environment. The key elements of any environment that AGV must identify are the drivable pathway and whether there are any obstacles around it. Many works have been published focusing on different detection components in various ways. In this paper, a survey of the most recent advancements in AGV detection methods that are intended specifically for the off-road environment has been presented. For this, we divided the literature into three major groups: drivable ground and positive and negative obstacles. Each detection portion has been further divided into multiple categories based on the technology used, for example, single sensor-based, multiple sensor-based, and how the data has been analyzed. Furthermore, it has added critical findings in detection technology, challenges associated with detection and off-road environment, and possible future directions. Authors believe this work will help the reader in finding literature who are doing similar works.

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Section: Positive Obstacles Detection and Analysismentioning

confidence: 99%

Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review

Islam¹,

Nabi²,

Ball³

2022

Sensors

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“…In Sha, Chen et al's work, road contours were extracted efficiently and based completely on a supervoxel method without any trajectory data [17]. The Euclidean clustering algorithm was optimized by supervoxels to improve the anti-noise ability of the clustering process by Chen et al [18]. Li, Liu et al proposed a multi-resolution supervoxels method to improve accuracy in regions of inconsistent density [19].…”

Section: Introductionmentioning

confidence: 99%

An Improved Supervoxel Clustering Algorithm of 3D Point Clouds for the Localization of Industrial Robots

Xie

Liang²,

Tao

et al. 2022

Electronics

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Supervoxels have a widespread application of instance segmentation on account of the merit of providing a highly approximate representation with fewer data. However, low accuracy, mainly caused by point cloud adhesion in the localization of industrial robots, is a crucial issue. An improved bottom-up clustering method based on supervoxels was proposed for better accuracy. Firstly, point cloud data were preprocessed to eliminate the noise points and background. Then, improved supervoxel over-segmentation with moving least squares (MLS) surface fitting was employed to segment the point clouds of workpieces into supervoxel clusters. Every supervoxel cluster can be refined by MLS surface fitting, which reduces the occurrence that over-segmentation divides the point clouds of two objects into a patch. Additionally, an adaptive merging algorithm based on fusion features and convexity judgment was proposed to accomplish the clustering of the individual workpiece. An experimental platform was set up to verify the proposed method. The experimental results showed that the recognition accuracy and the recognition rate in three different kinds of workpieces were all over 0.980 and 0.935, respectively. Combined with the sample consensus initial alignment (SAC-IA) coarse registration and iterative closest point (ICP) fine registration, the coarse-to-fine strategy was adopted to obtain the location of the segmented workpieces in the experiments. The experimental results demonstrate that the proposed clustering algorithm can accomplish the localization of industrial robots with higher accuracy and lower registration time.

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A Smartphone Based Real-Time Object Recognition System for Visually Impaired People

Rahman

Alam

2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Novel Laser-Based Obstacle Detection for Autonomous Robots on Unstructured Terrain

Cited by 11 publications

References 37 publications

Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review

Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review

An Improved Supervoxel Clustering Algorithm of 3D Point Clouds for the Localization of Industrial Robots

A Smartphone Based Real-Time Object Recognition System for Visually Impaired People

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