Abstract
LiDAR odometry is the key component of LiDAR-based simultaneous localization and mapping (SLAM). However, the low vertical resolution of LiDAR makes it difficult to produce pleasant mapping results. It is even more challenging to reconstruct the surface of dynamic objects from the raw LiDAR input. To address this problem, existing approaches typically divide it into several subproblems like object detection and tracking and then solve them individually, which greatly increases the complexity of LiDAR odometry as well as the SLAM framework. In this work, we propose to address this problem by improving LiDAR odometry with appropriate modifications to the depth fusion process and several additional lightweight components. Extensive evaluations on KITTI dataset and Velodyne HDL-16E laser scanner demonstrate the effectiveness of the proposed method. The results of the improved LiDAR odometry include abundant information about the dynamic objects, which can be used for many high-level tasks such as object recognition and scene understanding.
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Acknowledgment
This work is partially supported by the National Natural Science Foundation of China (NSFC) under Grant 61602533, NSFC-Shenzhen Robotics Projects (U1613211), The Fundamental Research Funds for the Central Universities, and Science and Technology Program of Guangzhou, China (201510010126).
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Cheng, H., Hu, Y., Huang, H., Chen, C., Chen, C. (2017). Reconstructing Dynamic Objects via LiDAR Odometry Oriented to Depth Fusion. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds) Intelligent Robotics and Applications. ICIRA 2017. Lecture Notes in Computer Science(), vol 10463. Springer, Cham. https://doi.org/10.1007/978-3-319-65292-4_55
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DOI: https://doi.org/10.1007/978-3-319-65292-4_55
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