KDD-LOAM: Jointly Learned Keypoint Detector and Descriptors Assisted
  LiDAR Odometry and Mapping
Renlang Huang Minglei Zhao Jiming Chen Liang Li
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.