3D LiDAR Driven Reinforcement Learning With Safety Intervention for Mapless Navigation

Abstract

Autonomous navigation in real-world public service, industrial inspection, and emergency response often faces frequent changes in nominally static scene structures, which can quickly invalidate pre-built global maps and naturally lead to a mapless navigation setting. We propose an end-to-end 3D LiDAR based navigation framework that directly maps raw point clouds and relative goal cues to discrete actions. First, an importance gaze point cloud representation (IGPR) converts unordered sparse scans into a compact two-channel image representation via view-region enhancement and an adaptive reciprocal factor, improving sensitivity to decision critical geometry. Second, a geometry-driven dense reward is designed from raw point cloud structure and goal relative states to accelerate reinforcement learning and mitigate local-optimum behaviors. Third, a raw point- cloud driven Gaussian Control Barrier Functions (RPGC) safety controller performs minimal intervention when unsafe proximity is detected, improving deployment robustness without overriding the learned policy. Extensive simulations across diverse obstacle styles and time varying layouts demonstrate consistently high success rates and stable path efficiency. Real world experiments with novel obstacle geometries not encountered in simulation further validate direct sim-to-real transfer of the learned policy without additional real-world training.