Improved VIDAR and machine learning-based road obstacle detection method

Wang, Yuqiong; Zhu, Ruoyu; Li-ming, Wang; Xu, Yi; Guo, Dong; Gao, Song

doi:10.1016/j.array.2023.100283

Cited by 1 publication

(1 citation statement)

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“…Barbosa [8] fused 2D LiDAR and camera data using a convolutional neural network for segmentation tasks, and improved the obstacle detection accuracy for driving vehicles under adverse weather and strong lighting conditions. Wang [9] proposed an obstacle detection method based on machine learning and improved VIDAR. Specific types of obstacles were detected using machine learning algorithms, and the improved VIDAR removed unknown obstacles from the image to increase detection speed (average detection time of 0.316 s) and performance (accuracy of 92.7%).…”

Section: Introductionmentioning

confidence: 99%

Advancing Simultaneous Localization and Mapping with Multi-Sensor Fusion and Point Cloud De-Distortion

Shao,

Zhao,

Chen

et al. 2023

Machines

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This study addresses the challenges associated with incomplete or missing information in obstacle detection methods that employ a single sensor. Additionally, it tackles the issue of motion distortion in LiDAR point cloud data during synchronization and mapping in complex environments. The research introduces two significant contributions. Firstly, a novel obstacle detection method, named the point-map fusion (PMF) algorithm, was proposed. This method integrates point cloud data from the LiDAR, camera, and odometer, along with local grid maps. The PMF algorithm consists of two components: the point-fusion (PF) algorithm, which combines LiDAR point cloud data and camera laser-like point cloud data through a point cloud library (PCL) format conversion and concatenation, and selects the most proximate point cloud to the quadruped robot dog as the valid data; and the map-fusion (MF) algorithm, which incorporates local grid maps acquired using the Gmapping and OctoMap algorithms, leveraging Bayesian estimation theory. The local grid maps obtained by the Gmapping and OctoMap algorithms are denoted as map A and map B, respectively. This sophisticated methodology enables seamless map fusion, which significantly enhances the precision and reliability of the approach. Secondly, a motion distortion removal (MDR) method for LiDAR point cloud data based on odometer readings was proposed. The MDR method utilizes legged odometer data for linear data interpolation of the original distorted LiDAR point cloud data, facilitating the determination of the corresponding pose of the quadruped robot dog. Subsequently, the LiDAR point cloud data are then transformed to the quadruped robot dog coordinate system, efficiently mitigating motion distortion. Experimental results demonstrated that the proposed PMF algorithm achieved a 50% improvement in success rate compared to using only LiDAR or the PF algorithm in isolation, while the MDR algorithm enhanced mapping accuracy by 45.9% when motion distortion was taken into account. The effectiveness of the proposed methods was confirmed through rigorous experimentation.

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Section: Introductionmentioning

confidence: 99%