3D Perception and Reconstruction System Based on 2D Laser Scanner

Chu, Hsin‐Sen; Gong, Kai; Shao, Yanhua; Chang, Ziwei; Ni, Junchao

doi:10.1109/cac.2018.8623391

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…With the rapid development of autonomous driving [1], the 3D perception has drawn increasing attention and shown great potential in the vehicle-mounted system [2,3], which is critical to localization [4,5], planning, and obstacle avoidance [6,7], etc., as shown in Figure 1. Among existing methods [8,9,10], they usually employ the homography transformation [8] of the wheel grounding points obtained from the wheel's detection results to estimate the localization of target vehicles with limited computing ability in the low cost environment. Consequently, it is necessary to introduce the owner-member relationship of wheels and vehicles to constrain the process.…”

Section: Introductionmentioning

confidence: 99%

DeepWORD: A GCN-Based Approach for Owner-Member Relationship Detection in Autonomous Driving

Wu¹,

Wang²,

Wang³

et al. 2021

2021 IEEE International Conference on Multimedia and Expo (ICME)

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It's worth noting that the owner-member relationship between wheels and vehicles has an significant contribution to the 3D perception of vehicles, especially in the embedded environment. However, there are currently two main challenges about the above relationship prediction: i) The traditional heuristic methods based on IoU can hardly deal with the traffic jam scenarios for the occlusion. ii) It is difficult to establish an efficient applicable solution for the vehicle-mounted system. To address these issues, we propose an innovative relationship prediction method, namely DeepWORD, by designing a graph convolution network (GCN). Specifically, we utilize the feature maps with local correlation as the input of nodes to improve the information richness. Besides, we introduce the graph attention network (GAT) to dynamically amend the prior estimation deviation. Furthermore, we establish an annotated owner-member relationship dataset called WORD as a large-scale benchmark, which will be available soon. The experiments demonstrate that our solution achieves state-ofthe-art accuracy and real-time in practice.

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Section: Introductionmentioning

confidence: 99%

DeepWORD: A GCN-Based Approach for Owner-Member Relationship Detection in Autonomous Driving

Wu¹,

Wang²,

Wang³

et al. 2021

2021 IEEE International Conference on Multimedia and Expo (ICME)

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Research on agricultural monitoring and progressive map creation in the context of ground-air coordination

2024

J. Phys.: Conf. Ser.

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By combining network RTK technology, 2D LiDAR, and UAV, this study investigates how to achieve agricultural monitoring effectively. A collaborative agricultural monitoring scheme was designed to accurately and comprehensively gather agricultural information in response to the demand for targeted sequential monitoring at specified agricultural locations. To establish a low-cost and low computational power-dependent 3D map of obstacle point clouds, a progressive 3D mapping scheme based on 2D LiDAR was designed and implemented. The effectiveness of the design was validated through simulation experiments and partly through real-world testing.

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DeepWORD: A GCN-based Approach for Owner-Member Relationship Detection in Autonomous Driving

Wu¹,

Wang²,

Wang³

et al. 2021

Preprint

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3D Perception and Reconstruction System Based on 2D Laser Scanner

Cited by 3 publications

References 18 publications

DeepWORD: A GCN-Based Approach for Owner-Member Relationship Detection in Autonomous Driving

DeepWORD: A GCN-Based Approach for Owner-Member Relationship Detection in Autonomous Driving

Research on agricultural monitoring and progressive map creation in the context of ground-air coordination

DeepWORD: A GCN-based Approach for Owner-Member Relationship Detection in Autonomous Driving

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