Teaching Robots to Say 'I Don't Know' : SENTINEL for Uncertainty-Aware SLAM
Overview
This research introduces SENTINEL, a training-free and label-free framework that enables low-cost 2D LiDAR sensors to detect measurement failures by combining geometry-based scan statistics with cross-modal depth consistency from an RGB-D camera. Evaluated on a GEFIER R1 robot in a physical arena containing transparent and reflective obstacles, the system computes per-scan reliability scores to reject corrupted data and fallback to wheel odometry, preventing silent SLAM corruption. Validation was performed entirely on real hardware due to the absence of these failure modes in simulation.
Generated from attributed reports · Updated 34 minutes ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · arena dimensions: 185 other · Basis not reported · Differing source assertions
“435i 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”
Exact source · revision 1Source owner not reported
Reported quantity · arena dimensions: 245 other · Basis not reported · Differing source assertions
“435i 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”
Exact source · revision 1Source owner not reported
Reported quantity · reliability score range: 0 other · Basis not reported · Differing source assertions
“tistics 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 D”
Exact source · revision 1Source owner not reported
Reported quantity · reliability score range: 1 other · Basis not reported · Differing source assertions
“tistics 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 D”
Exact source · revision 1Source owner not reported
Reported quantity · camera type: 0 other · Basis not reported
“tistics 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 D”
Exact source · revision 1Source owner not reported
Dataset · modalities: LiDAR
“arXiv:2606.04853v2 Announce Type: replace Abstract: 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 sta”
Exact source · revision 1Source owner not reported
Dataset · tasks: SLAM · Differing source assertions
“Teaching Robots to Say 'I Don't Know' : SENTINEL for Uncertainty-Aware SLAM”
Exact source · revision 1Source owner not reported
Dataset · tasks: reliability estimation · Differing source assertions
“arXiv:2606.04853v2 Announce Type: replace Abstract: 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 sta”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalTeaching Robots to Say 'I Don't Know' : SENTINEL for Uncertainty-Aware SLAM
This research introduces SENTINEL, a training-free and label-free framework that enables low-cost 2D LiDAR sensors to detect measurement failures by combining geometry-based scan statistics with cross-modal depth consistency from an RGB-D camera. Evaluated on a GEFIER R1 robot in a physical arena containing transparent and reflective obstacles, the system computes per-scan reliability scores to reject corrupted data and fallback to wheel odometry, preventing silent SLAM corruption. Validation was performed entirely on real hardware due to the absence of these failure modes in simulation.
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