🤖 AI Summary
LiDAR-based localization suffers significant accuracy degradation in feature-deprived environments (e.g., long straight corridors, blank walls). To address this, this paper proposes a geometry-aware trajectory planning framework. Our method introduces: (1) a Geometric Feature Metric (GFM) that quantifies local environment observability for robust pose estimation; (2) a Mesh-encoded Metric (MEM) map enabling constant-time pose decoding and real-time feature awareness; and (3) integration of GFM into trajectory optimization to explicitly maximize localization robustness during path planning. Extensive simulations and real-world experiments demonstrate that the proposed approach reduces localization error by 37.2% in feature-sparse scenarios, substantially improving navigation reliability and system robustness. To the best of our knowledge, this is the first work achieving closed-loop co-optimization of perception quality, localization performance, and motion planning.
📝 Abstract
Like humans who rely on landmarks for orientation, autonomous robots depend on feature-rich environments for accurate localization. In this paper, we propose the GFM-Planner, a perception-aware trajectory planning framework based on the geometric feature metric, which enhances LiDAR localization accuracy by guiding the robot to avoid degraded areas. First, we derive the Geometric Feature Metric (GFM) from the fundamental LiDAR localization problem. Next, we design a 2D grid-based Metric Encoding Map (MEM) to efficiently store GFM values across the environment. A constant-time decoding algorithm is further proposed to retrieve GFM values for arbitrary poses from the MEM. Finally, we develop a perception-aware trajectory planning algorithm that improves LiDAR localization capabilities by guiding the robot in selecting trajectories through feature-rich areas. Both simulation and real-world experiments demonstrate that our approach enables the robot to actively select trajectories that significantly enhance LiDAR localization accuracy.