让机器人绕开感知差区域，主动选好路提升定位精度
GFM-Planner: Perception-Aware Trajectory Planning with Geometric Feature Metric
- 用几何特征度量引导机器人避开定位差区域
- 实测轨迹规划后定位误差降低，真实场景验证有效
- 适合需要高精度定位的自动驾驶与机器人导航
如同人类依赖地标导航，自主机器人也依赖特征丰富的环境实现精准定位。本文提出GFM-Planner，一种基于几何特征度量的感知感知轨迹规划框架，通过引导机器人避开低特征区域来提升激光雷达（LiDAR）定位精度。首先，从基础的LiDAR定位问题推导出几何特征度量（GFM）。其次，设计基于二维栅格的度量编码地图（MEM），高效存储环境中各位置的GFM值，并提出常数时间解码算法，可快速获取任意位姿下的GFM值。最后，构建感知感知轨迹规划算法，引导机器人选择经过高特征区域的路径，从而增强定位能力。仿真与真实世界实验均表明，该方法使机器人能主动选择显著提升定位精度的轨迹。
原文摘要 · Abstract (English)
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.
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