KDD-LOAM: Jointly Learned Keypoint Detector and Descriptors Assisted LiDAR Odometry and Mapping

• URL: http://arxiv.org/abs/2309.15394v1
• Date: Wed, 27 Sep 2023 04:10:52 GMT
• Title: KDD-LOAM: Jointly Learned Keypoint Detector and Descriptors Assisted LiDAR Odometry and Mapping
• Authors: Renlang Huang, Minglei Zhao, Jiming Chen, and Liang Li
• Abstract summary: We propose a tightly coupled keypoint detector and descriptor based on a multi-task fully convolutional network with a probabilistic detection loss. Experiments on both indoor and outdoor datasets show that our TCKDD achieves state-of-the-art performance in point cloud registration. We also design a keypoint detector and descriptors-assisted LiDAR odometry and mapping framework (KDD-LOAM), whose real-time odometry relies on keypoint descriptor matching-based RANSAC.
• Score: 9.609585217048664
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sparse keypoint matching based on distinct 3D feature representations can improve the efficiency and robustness of point cloud registration. Existing learning-based 3D descriptors and keypoint detectors are either independent or loosely coupled, so they cannot fully adapt to each other. In this work, we propose a tightly coupled keypoint detector and descriptor (TCKDD) based on a multi-task fully convolutional network with a probabilistic detection loss. In particular, this self-supervised detection loss fully adapts the keypoint detector to any jointly learned descriptors and benefits the self-supervised learning of descriptors. Extensive experiments on both indoor and outdoor datasets show that our TCKDD achieves state-of-the-art performance in point cloud registration. Furthermore, we design a keypoint detector and descriptors-assisted LiDAR odometry and mapping framework (KDD-LOAM), whose real-time odometry relies on keypoint descriptor matching-based RANSAC. The sparse keypoints are further used for efficient scan-to-map registration and mapping. Experiments on KITTI dataset demonstrate that KDD-LOAM significantly surpasses LOAM and shows competitive performance in odometry.

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