TY - GEN
T1 - αliDAR
T2 - 30th International Conference on Mobile Computing and Networking, ACM MobiCom 2024
AU - Cui, Jiahe
AU - He, Yuze
AU - Niu, Jianwei
AU - Ouyang, Zhenchao
AU - Xing, Guoliang
N1 - Publisher Copyright:
© 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.
PY - 2024/12/4
Y1 - 2024/12/4
N2 - LiDAR technology holds vast potential across various sectors, including robotics, autonomous driving, and urban planning. However, the performance of current LiDAR sensors is hindered by limited field of view (FOV), low resolution, and lack of flexible focusing capability. We introduce αLiDAR, an innovative LiDAR system that employs controllable actuation to provide a panoramic FOV, high resolution, and adaptable scanning focus. The core concept of αLiDAR is to expand the operational freedom of a LiDAR sensor through the incorporation of a controllable, active rotational mechanism. This modification allows the sensor to scan previously inaccessible blind spots and focus on specific areas of interest in an adaptive manner. By modeling uncertainties in LiDAR rotation process and estimating point-wise uncertainty, αLiDAR can correct point cloud distortions resulted from significant rotation. In addition, by optimizing LiDAR's rotation trajectory, αLiDAR can swiftly adapt to dynamic areas of interest. We developed several prototypes of αLiDAR and conducted comprehensive evaluations in various indoor and outdoor real-world scenarios. Our results demonstrate that αLiDAR achieves centimeter-level pose estimation accuracy, with an average latency of only 37 ms. In two typical LiDAR applications, αLiDAR significantly enhances 3D mapping accuracy, coverage, and density by 8.5×, 2×, and 1.6× respectively, compared to conventional LiDAR sensors. Additionally, αLiDAR's adaptive rotation improves the effective sensing distance by 1.8× and increases the number of perceived objects by 1.9×. A video demonstration of αLiDAR's in action in real world is available at https://youtu.be/x4zc_I_xTaw. The code is available at https://github.com/HViktorTsoi/alpha_lidar.
AB - LiDAR technology holds vast potential across various sectors, including robotics, autonomous driving, and urban planning. However, the performance of current LiDAR sensors is hindered by limited field of view (FOV), low resolution, and lack of flexible focusing capability. We introduce αLiDAR, an innovative LiDAR system that employs controllable actuation to provide a panoramic FOV, high resolution, and adaptable scanning focus. The core concept of αLiDAR is to expand the operational freedom of a LiDAR sensor through the incorporation of a controllable, active rotational mechanism. This modification allows the sensor to scan previously inaccessible blind spots and focus on specific areas of interest in an adaptive manner. By modeling uncertainties in LiDAR rotation process and estimating point-wise uncertainty, αLiDAR can correct point cloud distortions resulted from significant rotation. In addition, by optimizing LiDAR's rotation trajectory, αLiDAR can swiftly adapt to dynamic areas of interest. We developed several prototypes of αLiDAR and conducted comprehensive evaluations in various indoor and outdoor real-world scenarios. Our results demonstrate that αLiDAR achieves centimeter-level pose estimation accuracy, with an average latency of only 37 ms. In two typical LiDAR applications, αLiDAR significantly enhances 3D mapping accuracy, coverage, and density by 8.5×, 2×, and 1.6× respectively, compared to conventional LiDAR sensors. Additionally, αLiDAR's adaptive rotation improves the effective sensing distance by 1.8× and increases the number of perceived objects by 1.9×. A video demonstration of αLiDAR's in action in real world is available at https://youtu.be/x4zc_I_xTaw. The code is available at https://github.com/HViktorTsoi/alpha_lidar.
KW - LiDAR
KW - active sensing
KW - pose estimation
KW - sensor fusion
KW - state estimation
UR - https://www.scopus.com/pages/publications/105002557753
U2 - 10.1145/3636534.3690708
DO - 10.1145/3636534.3690708
M3 - 会议稿件
AN - SCOPUS:105002557753
T3 - ACM MobiCom 2024 - Proceedings of the 30th International Conference on Mobile Computing and Networking
SP - 1515
EP - 1529
BT - ACM MobiCom 2024 - Proceedings of the 30th International Conference on Mobile Computing and Networking
PB - Association for Computing Machinery, Inc
Y2 - 18 November 2024 through 22 November 2024
ER -