IndoorBEV: Joint Detection and Footprint Completion of Objects via Mask-based Prediction in Indoor Scenarios for Bird's-Eye View Perception

The deployment of autonomous robots in indoor environments necessitates precise and real-time perception of their surroundings to ensure safe and efficient navigation. Lidar sensors have emerged as a pivotal technology in this domain, offering high-resolution 3D point cloud data that is robust to varying lighting conditions and capable of capturing intricate spatial details. However, transforming this unstructured point cloud data into actionable representations for tasks such as object detection, segmentation, and navigation remains a formidable challenge, particularly given the complexity and clutter often found indoors. Recent advancements have sought to address these challenges. For instance, MakeWay [1] system introduces object-aware costmaps derived from lidar data to enhance proactive indoor navigation. Similarly, the L V -DOT framework [2] leverages a fusion of lidar and visual data to improve dynamic obstacle detection and tracking in indoor settings. These approaches underscore the potential of integrating machine learning techniques with lidar data to enhance indoor perception. Bird's-Eye View (BEV) representations naturally handles occlusions and provides a representation directly amenable to downstream robotic tasks like navigation and planning due to their ability to provide a top-down, spatially consistent view of the environment.