Teaching Robots to Say 'I Don't Know' : SENTINEL for Uncertainty-Aware SLAM
For low-cost robotics platforms using 2D LiDAR, SENTINEL addresses the problem of silent SLAM corruption due to undiagnosed measurement failures, offering a practical solution without requiring training data or labels.
SENTINEL provides a training-free, label-free method for 2D LiDAR reliability estimation by combining geometry-based scan statistics with cross-modal depth consistency, enabling rejection of corrupted scans and fallback to wheel odometry. Real-world tests on a robot with transparent and reflective obstacles show clear separation between clean and failure cases.
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.