From Depth to Safe Depth: Uncertainty-Aware Monocular Perception for Mobile Robot Navigation
Overview
This research paper introduces a lightweight plug-in module that transforms predictions from standard monocular depth foundation models into 'safe depth' estimates by incorporating uncertainty. The authors demonstrate that while off-the-shelf models like Depth Anything V2 lead to high collision rates (63%) in simulated navigation tasks due to distance over-estimation, their proposed method reduces collisions to 3% and improves navigation success from 37% to 70%. The approach uses a frozen depth model with a small correction head trained via conformal calibration to adjust depth values pixel-wise, effectively handling challenging conditions like glass surfaces and texture-less walls.
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Event evidence and corrections
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Environment: simulation
“arXiv:2611.13769v1 Announce Type: new Abstract: Monocular depth foundation models make it tempting to replace depth sensors on mobile robots with a single RGB camera. We show that, used off the shelf, this is unsafe: in a simulated indoor navigation benchmark, a standard Nav2 stack driven by Depth Anything V2 (metric) collides in 63\% of 120 episodes, because the model systematically over-estimates the distance to ne”
Exact source · revision 1Source owner not reported
Reported quantity · episode count: 120 other · Basis not reported
“arXiv:2611.13769v1 Announce Type: new Abstract: Monocular depth foundation models make it tempting to replace depth sensors on mobile robots with a single RGB camera. We show that, used off the shelf, this is unsafe: in a simulated indoor navigation benchmark, a standard Nav2 stack driven by Depth Anything V2 (metric) collides in 63\% of 120 episodes, because the model systematically over-estimates the distance to ne”
Exact source · revision 1Source owner not reported
Reported quantity · depth error AbsRel: 0.262 other · Basis not reported
“is set by conformal calibration on a held-out world, and uncertainty also decides which pixels may be treated as floor. The correction halves the depth error (AbsRel $0.262\rightarrow0.134$) and reduces near-range over-estimation from 59.8\% to 12.0\%. In navigation, safe depth raises the success rate from 37\% to 70\% and lowers collisions from 63\% to 3\%, close to the 2\% of ground-truth depth. A constant safety m”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalFrom Depth to Safe Depth: Uncertainty-Aware Monocular Perception for Mobile Robot Navigation
This research paper introduces a lightweight plug-in module that transforms predictions from standard monocular depth foundation models into 'safe depth' estimates by incorporating uncertainty. The authors demonstrate that while off-the-shelf models like Depth Anything V2 lead to high collision rates (63%) in simulated navigation tasks due to distance over-estimation, their proposed method reduces collisions to 3% and improves navigation success from 37% to 70%. The approach uses a frozen depth model with a small correction head trained via conformal calibration to adjust depth values pixel-wise, effectively handling challenging conditions like glass surfaces and texture-less walls.
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