Pose Estimation of Moving Vehicles Based on Heuristic Rules for Autonomous Driving

He, Yuesheng; Zhang, Wei; Yang, Ming

doi:10.1109/robio55434.2022.10011929

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…The other strategy selects the optimal bounding box by minimizing the area of the rectangle fitted with the convex hull edges. He et al [31] presented a 3D pose estimation model based on the heuristic rules. It considers both the edge length and edge visibility to filter the convex hull-based orientations, and the tracking results are also used to smooth the orientation estimates.…”

Section: Model-based Methodsmentioning

confidence: 99%

Dynamic Vehicle Pose Estimation with Heuristic L-Shape Fitting and Grid-Based Particle Filter

Sun

Liu

2023

Electronics

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Vehicle pose estimation with LIDAR plays a crucial role in autonomous driving systems. It serves as the fundamental basis for functions such as tracking, path planning, and decision-making. However, the majority of current vehicle pose estimation techniques struggle to produce satisfactory results when faced with incomplete observation measurements, such as L-shaped point cloud clusters without side contours or those including side-view mirrors. In addition, the requirement for real-time results further increases the difficulty of the pose estimation task. In this paper, we present a vehicle Pose Estimation method with Heuristic L-shape fitting and grid-based Particle Filter (PE-HL-PF). We design a geometric shape classifier module to divide clusters into symmetrical and asymmetrical ones according to their shape features. Furthermore, a contour-based heuristic L-shape fitting module is introduced for asymmetrical clusters, and a structure-aware grid-based particle filter is used to estimate the pose of symmetrical clusters. PE-HL-PF first utilizes a heuristic asymmetrical module that selects dominant contours fitting orientation in a heuristic manner, thereby avoiding the need for a complex traversal search. Additionally, a symmetrical module based on particle filtering is incorporated to enhance the stability of orientation estimation. This method achieves significant improvements in both the runtime efficiency and pose estimation accuracy of incomplete point clouds. Compared with state-of-the-art pose estimation methods, our PE-HL-PF demonstrates a notable performance improvement. Our method can estimate the pose of thousands of objects in less than 1 millisecond, a significant improvement over previous methods. The results of experiments performed on the KITTI dataset validate the effectiveness of our approach.

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Section: Model-based Methodsmentioning

confidence: 99%

Dynamic Vehicle Pose Estimation with Heuristic L-Shape Fitting and Grid-Based Particle Filter

Sun

Liu

2023

Electronics

View full text Add to dashboard Cite

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Dueling-DQN-Assisted Beam Hopping and Power Allocation with Dynamic Cache for LEO Satellite Communications

Hu,

Ma,

Xiao

2023

2023 3rd International Conference on Electronic Information Engineering and Computer Communication (EIECC)

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Efficient Convex-Hull-Based Vehicle Pose Estimation Method for 3D LiDAR

Ding,

Ming,

Wang

2024

Transportation Research Record: Journal of the Transportation R

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Vehicle pose estimation with light detection and ranging (LiDAR) is essential in the perception technology of autonomous driving. However, because of incomplete observation measurements and sparsity of the LiDAR point cloud, it is challenging to achieve satisfactory pose extraction based on 3D LiDAR with the existing pose estimation methods. In addition, the demand for real-time performance further increases the difficulty of the pose estimation task. In this paper, we propose a novel vehicle pose estimation method based on the convex hull. The extracted 3D cluster is reduced to the convex hull, reducing the subsequent computation burden while preserving essential contour information. Subsequently, a novel criterion based on the minimum occlusion area is developed for the search-based algorithm, enabling accurate pose estimation. Additionally, this criterion renders the proposed algorithm particularly well-suited for obstacle avoidance. The proposed algorithm is validated on the KITTI dataset and a manually labeled dataset acquired at an industrial park. The results demonstrate that our proposed method can achieve better accuracy than the classical pose estimation method while maintaining real-time speed.

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Pose Estimation of Moving Vehicles Based on Heuristic Rules for Autonomous Driving

Cited by 3 publications

References 19 publications

Dynamic Vehicle Pose Estimation with Heuristic L-Shape Fitting and Grid-Based Particle Filter

Dynamic Vehicle Pose Estimation with Heuristic L-Shape Fitting and Grid-Based Particle Filter

Dueling-DQN-Assisted Beam Hopping and Power Allocation with Dynamic Cache for LEO Satellite Communications

Efficient Convex-Hull-Based Vehicle Pose Estimation Method for 3D LiDAR

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