UniLGL: Learning Uniform Place Recognition for FOV-limited/Panoramic LiDAR Global Localization

Existing LGL methods typically consider only partial information (e.g., geometric features) from LiDAR observations or are designed for homogeneous LiDAR sensors, overlooking the uniformity in LGL. In this work, a uniform LGL method is proposed, termed UniLGL, which simultaneously achieves spatial and material uniformity, as well as sensor-type uniformity. The key idea of the proposed method is to encode the complete point cloud, which contains both geometric and material information, into a pair of BEV images (i.e., a spatial BEV image and an intensity BEV image). An end-to-end multi-BEV fusion network is designed to extract uniform features, equipping UniLGL with spatial and material uniformity. To ensure robust LGL across heterogeneous LiDAR sensors, a viewpoint invariance hypothesis is introduced, which replaces the conventional translation equivariance assumption commonly used in existing LPR networks and supervises UniLGL to achieve sensor-type uniformity in both global descriptors and local feature representations. Finally, based on the mapping between local features on the 2D BEV image and the point cloud, a robust global pose estimator is derived that determines the global minimum of the global pose on SE(3) without requiring additional registration. To validate the effectiveness of the proposed uniform LGL, extensive benchmarks are conducted in real-world environments, and the results show that the proposed UniLGL is demonstratively competitive compared to other State-of-the-Art LGL methods. Furthermore, UniLGL has been deployed on diverse platforms, including full-size trucks and agile Micro Aerial Vehicles (MAVs), to enable high-precision localization and mapping as well as multi-MAV collaborative exploration in port and forest environments, demonstrating the applicability of UniLGL in industrial and field scenarios.

YOPO-Rally: A Sim-to-Real Single-Stage Planner for Off-Road Terrain

Off-road navigation remains challenging for autonomous robots due to the harsh terrain and clustered obstacles. In this letter, we extend the YOPO (You Only Plan Once) end-to-end navigation framework to off-road environments, explicitly focusing on forest terrains, consisting of a high-performance, multi-sensor supported off-road simulator YOPO-Sim, a zero-shot transfer sim-to-real planner YOPO-Rally, and an MPC controller. Built on the Unity engine, the simulator can generate randomized forest environments and export depth images and point cloud maps for expert demonstrations, providing competitive performance with mainstream simulators. Terrain Traversability Analysis (TTA) processes cost maps, generating expert trajectories represented as non-uniform cubic Hermite curves. The planner integrates TTA and the pathfinding into a single neural network that inputs the depth image, current velocity, and the goal vector, and outputs multiple trajectory candidates with costs. The planner is trained by behavior cloning in the simulator and deployed directly into the real-world without fine-tuning. Finally, a series of simulated and real-world experiments is conducted to validate the performance of the proposed framework.

YOPOv2-Tracker: An End-to-End Agile Tracking and Navigation Framework from Perception to Action

Traditional target tracking pipelines including detection, mapping, navigation, and control are comprehensive but introduce high latency, limitting the agility of quadrotors. On the contrary, we follow the design principle of "less is more", striving to simplify the process while maintaining effectiveness. In this work, we propose an end-to-end agile tracking and navigation framework for quadrotors that directly maps the sensory observations to control commands. Importantly, leveraging the multimodal nature of navigation and detection tasks, our network maintains interpretability by explicitly integrating the independent modules of the traditional pipeline, rather than a crude action regression. In detail, we adopt a set of motion primitives as anchors to cover the searching space regarding the feasible region and potential target. Then we reformulate the trajectory optimization as regression of primitive offsets and associated costs considering the safety, smoothness, and other metrics. For tracking task, the trajectories are expected to approach the target and additional objectness scores are predicted. Subsequently, the predictions, after compensation for the estimated lumped disturbance, are transformed into thrust and attitude as control commands for swift response. During training, we seamlessly integrate traditional motion planning with deep learning by directly back-propagating the gradients of trajectory costs to the network, eliminating the need for expert demonstration in imitation learning and providing more direct guidance than reinforcement learning. Finally, we deploy the algorithm on a compact quadrotor and conduct real-world validations in both forest and building environments to demonstrate the efficiency of the proposed method.