L4Dog: Towards BEV Perception for Quadruped Robots in Complex Urban Scenes

Chang Liu, Mingxu Zhu, Qingliang Luo, Zheyuan Zhang, Xiao Zhao, Linna Song, Zhe Ren, Chufan Guo, Kuifeng Su

Keywords: quadruped robot perception, dataset and benchmark

Abstract: Embodied intelligence in quadruped robots faces significant challenges in complex urban environments due to the limitations of traditional perception systems and the lack of comprehensive datasets for exteroceptive 3D perception. To address this, we introduce L4Dog, the first large-scale exteroceptive 3D perception dataset tailored for quadruped robots in open urban scenarios. L4Dog provides high-quality 360-degree surround-view sensor data and manual annotations, covering diverse urban scenes such as traffic-light intersections, open roads, subway station, etc. By formulating perception tasks as bird’s-eye-view (BEV) space perception problems, we establish a multi-benchmark framework for BEV detection, tracking, trajectory prediction, and 3D traversable space occupancy estimation. The OmniBEV4D baseline method is proposed to unify multi-task perception (detection, tracking, prediction, and occupancy prediction) through shared temporal BEV features, enabling efficient and robust processing of dynamic urban environments. This work bridges the gap between current research and real-world deployment needs, offering a foundational resource for advancing autonomous navigation and decision-making in complex urban settings. The dataset will be made publicly available upon acceptance of this work.

Primary Area: datasets and benchmarks

Submission Number: 6807