让低成本激光雷达学会识别不可靠数据，防止机器人地图出错。
Teaching Robots to Say 'I Don't Know' : SENTINEL for Uncertainty-Aware SLAM
- 结合几何统计与多模态深度一致性计算每帧扫描可靠性
- 在玻璃、镜面等干扰表面下，可靠度评分可区分正常与失效数据
- 无需训练和标注，适合教育类和预算型机器人平台使用
低成本2D激光雷达缺乏高阶传感器的强度通道来诊断测量失败，但广泛用于教育和预算机器人平台。本文提出SENTINEL，一种无需训练、无需标签的可靠性评估框架，为仅含距离信息的激光雷达提供有效的诊断信号。SENTINEL融合几何扫描统计与激光雷达和RGB-D相机间的跨模态深度一致性，计算每帧扫描的0到1之间可靠性分数。当分数低于阈值时，系统拒绝异常扫描并切换至校准轮式里程计，避免无声的SLAM错误。我们在配备RPLidar A2M12和Intel RealSense D435i的GEFIER R1四轮滑移转向机器人上进行测试，实验区域为185×245厘米，中央障碍物设有可控透明与反射性故障元素。在五种表面条件下（包括玻璃、镜面、反光纸及混合镜面-反光纸），空间可靠性图谱清晰区分正常与失效情况，可准确识别需拒收或滤除的区域。由于这些故障模式无法在仿真中复现，验证全程基于真实硬件完成。
原文摘要 · Abstract (English)
Low-cost 2D LiDARs lack the intensity channel that higher-end sensors use to diagnose measurement failures, yet they are widely used on educational and budget robotics platforms. We present SENTINEL, a training - free, label - free reliability estimation framework that gives range - only LiDAR an effective diagnostic signal. SENTINEL combines geometry-based scan statistics with cross - modal depth consistency between LiDAR and an RGB - D camera to compute a per - scan reliability score between 0 and 1. When the score falls below a threshold, corrupted scans are rejected and the robot falls back to calibrated wheel odometry, preventing silent SLAM corruption. We evaluate SENTINEL on a GEFIER R1 four - wheel skid-steer robot equipped with an RPLidar A2M12 and an Intel RealSense D435i in a 185 cm by 245 cm arena containing controlled transparent and reflective failure elements on a central obstacle. Spatial reliability maps across five surface conditions, including glass, mirror, shiny paper, and a mixed mirror and shiny-paper condition, show clear separation between clean and failure cases, allowing affected regions to be identified as reject or noise. Because these failure modes are absent in simulation, validation is performed entirely on real hardware.
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