Robust and Real-Time Perception and Planning for UGVs in Complex Outdoor Environments

Abstract

Large-scale outdoor navigation is essential for unmanned ground vehicles (UGVs), but despite significant advancements, they still face two key challenges in practical applications. The first one is how to ensure safe navigation in environments with dynamic and low-lying obstacles that LiDAR cannot detect. The second one is how to conduct the adaptive re-planning of target points while some of them are blocked by temporary obstacles. To address these challenges, this work proposes a Dynamic and Low-lying-obstacle Avoidance Navigation (DLAN) system to conduct perception, planning, and point correction for UGVs. To efficiently and accurately detect dynamic obstacles, it designs a lightweight Ensemble3D framework that integrates three fast but low-accuracy detection methods. A multi-criteria waypoint optimizer is used to assist UGVs in path planning. It ensures a balance between obstacle avoidance and path following. To adjust blocked target points through local re-planning, this work designs a checkpoint correction method. Extensive simulations and real-world experiments demonstrate that DLAN enables reliable navigation with high efficiency and robust obstacle avoidance in complex environments. More details can be found on our project homepage and video <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>.