The registration of point clouds is a key ingredient of LiDAR-based SLAM systems and mapping approaches. A challenging task in this context is finding the right data association between 3D points. This paper proposes a novel and flexible graph network architecture to tackle the keypoint matching problem in an end-to-end fashion. Each layer of our multiplex dynamic graph attention network (MDGAT) utilizes an attention mechanism to dynamically construct a multiplex graph and reasons about the contextual information based on the point cloud data. It enriches the feature representation by recovering local information and by aggregating information along with the connections. We also design a scan matcher called MDGAT-matcher, which treats the registration problem as an optimal transport problem and uses the predictions of MDGAT as the cost. It builds upon sparse keypoints extracted from pairs of LiDAR scans. Eventually, MDGAT-matcher finds high-quality correspondences and at the same time handles non-matching points appropriately. We train our matcher using a novel gap loss guiding the network to learn a discriminative cognition about matching and non-matching 3D points. We thoroughly test our approach on the KITTI odometry benchmark. The experiments presented in this paper suggest that our approach outperforms state-of-the-art matching approaches and achieves a substantial improvement.

In this paper, we apply deep reinforcement learning (DRL) to solve the flocking control problem of multi-robot systems in complex environments with dynamic obstacles. Starting from the traditional flocking model, we propose a DRL framework for implementing multi-robot flocking control, eliminating the tedious work of modeling and control designing. We adopt the multi-agent deep deterministic policy gradient (MADDPG) algorithm, which additionally uses the information of multiple robots in the learning process to better predict the actions that robots will take. To address the problems such as low learning efficiency and slow convergence speed of the MADDPG algorithm, this paper studies a prioritized experience replay (PER) mechanism and proposes the Prioritized Experience Replay-MADDPG (PER-MADDPG) algorithm. Based on the temporal difference (TD) error, a priority evaluation function is designed to determine which experiences are sampled preferentially from the replay buffer. In the end, the simulation results verify the effectiveness of the proposed algorithm. It has a faster convergence speed and enables the robot group to complete the flocking task in the environment with obstacles.

LiDAR-based scene flow estimation (SFE) and moving object segmentation (MOS) are important tasks with broad-ranging applications in autonomous driving, such as traffic surveillance, motion analysis, obstacle avoidance, etc. Most existing works address SFE and MOS separately, ignoring the underlying shared geometric constraints and their inherent correlation. This article rethinks LiDAR-based SFE and MOS tasks, providing our key insight that jointly addressing them can tackle challenges in both tasks, and their solutions can reinforce one another to improve the performance of both. Based on this insight, we introduce a novel framework that exploits shared geometric constraints by explicitly partitioning the scene into static and moving regions and subsequently estimating flow differently for these regions. A lightweight and interpretable neural network dubbed is proposed. It employs an encoder and two specially designed head modules for each task, achieving MOS without relying on prior poses and online point-wise flow estimation for 360-degree point clouds. Due to the absence of public datasets for concurrently evaluating both tasks, we generate ground truth flow data using MOS labels from SemanticKITTI. Additionally, we establish a new dataset using a rotational LiDAR mounted on our own autonomous vehicle. Evaluation results on both datasets validate the superior performance of our proposed . Our dataset and label generation method are released at https://github.com/nubot-nudt/SFEMOS.