KDD-LOAM: Jointly Learned Keypoint Detector and Descriptors Assisted LiDAR Odometry and Mapping (2309.15394v1)

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.

• The paper presents a novel tightly-coupled keypoint detector and descriptor using a self-supervised probabilistic loss to improve LiDAR odometry and mapping.
• It integrates RANSAC-based matching with sparse keypoints for efficient scan-to-map registration and enhanced real-time performance.
• Extensive evaluations on 3DMatch and KITTI datasets demonstrate superior feature matching recall and registration accuracy compared to traditional methods.

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

In the domain of robotics and 3D vision, point cloud registration plays a pivotal role, especially in applications such as SLAM and 3D reconstruction. The paper "KDD-LOAM: Jointly Learned Keypoint Detector and Descriptors Assisted LiDAR Odometry and Mapping," presents an innovative approach to improving the efficiency and robustness of point cloud registration. The authors introduce a tightly coupled keypoint detector and descriptor (TCKDD) utilizing a multi-task fully convolutional network, which integrates a probabilistic detection loss for self-supervised learning.

Key Contributions

The presented research introduces a novel approach to enhance LiDAR odometry by integrating a tightly coupled keypoint detector and descriptor. The primary contributions of the paper are:

1. Tight Coupling of Detection and Description: The paper proposes a method to tightly couple 3D keypoint detection and description. By utilizing a self-supervised detection loss, the proposed model adapts the keypoint detector to the descriptors it learns, enhancing the self-supervised descriptor learning process.
2. Efficient LiDAR Odometry and Mapping Framework (KDD-LOAM): The authors introduce KDD-LOAM, which leverages the learned keypoint descriptors for real-time odometry. The framework incorporates RANSAC-based keypoint descriptor matching for odometry and employs sparse keypoints for efficient scan-to-map registration and mapping.
3. Probabilistic Detection Loss: The research formulates a novel probabilistic detection loss designed through maximum likelihood estimation, enabling the network to predict and utilize point-wise matchability for keypoint selection, concentrating learning efforts on salient regions.

Experimental Evaluation

The TCKDD framework is comprehensively evaluated on indoor (3DMatch) and outdoor (KITTI) datasets. The extensive experiments validate the state-of-the-art performance of TCKDD in point cloud registration tasks. Specifically, within indoor scenes, TCKDD demonstrates superior feature matching recall (FMR) and registration recall (RR) compared to both handcrafted and learning-based descriptors.

In outdoor scenarios on the KITTI dataset, TCKDD again shows remarkable results, achieving competitive relative translation error (RTE) and relative rotation error (RRE) performances, as well as a perfect registration recall, thereby substantiating its robustness and adaptability across varied conditions.

The KDD-LOAM system is further compared to existing LiDAR odometry and SLAM systems, such as LOAM, F-LOAM, SuMa, SuMa++, and KISS-ICP. The proposed system significantly surpasses classical approaches in terms of precision, maintaining competitive performance while also enhancing computational efficiency, especially in mapping and memory usage.

Implications and Future Work

This work presents significant implications for the robustness and efficiency of point cloud-based mapping and localization systems in both static and dynamic environments. By providing a method that tightly integrates keypoint detection with descriptor learning, the proposed approach mitigates limitations associated with separate learning strategies or handcrafted features and enhances both recall and accuracy in 3D data registration tasks.

Future developments may explore incorporating KDD-LOAM within broader SLAM frameworks, potentially enabling end-to-end learning systems that integrate loop closure detection and refined global pose graph optimization. Additionally, the framework could be extended or adapted for use with alternative sensors or within multi-sensory systems, expanding its applicability in mobile robotics and autonomous navigation.

In conclusion, the KDD-LOAM paper introduces a comprehensive and robust approach to LiDAR odometry, setting a noteworthy direction for future developments in the field of 3D computer vision and robotic perception systems.