Teaching Robots to Say 'I Don't Know' : SENTINEL for Uncertainty-Aware SLAM
Teaching 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.
The study validates a reliability estimation framework for low-cost LiDAR sensors using real-world hardware tests on reflective and transparent surfaces, demonstrating that geometry-based statistics combined with RGB-D consistency can effectively reject corrupted scans without training or labels.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
arena dimensions
185
View original evidence
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
Open source S4arena dimensions
245
View original evidence
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
Open source S4reliability score range
0
View original evidence
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
Open source S3reliability score range
1
View original evidence
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
Open source S3camera type
0
View original evidence
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
Open source S3
Dataset details reported by the source
- Modalities
- LiDAR Open source S2
- Tasks
- SLAM Open source S1reliability estimation Open source S2
- paper: not_reported
What remains unknown
Dataset licensing and commercial-use rights are not established here.
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
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
Open source S4
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
Open source S2
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
Open source S3
Teaching Robots to Say 'I Don't Know' : SENTINEL for Uncertainty-Aware SLAM
Open source S1
Implications for data suppliers
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- Check the exact dataset version, license, modalities and task coverage against the intended use.
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Source：arXiv Robotics — research abstracts · arxiv.org