MOSU2: Long-range Navigation System with Multi-modal Scene Understanding
Overview
This research presents MOSU2, a long-range navigation system for robots operating in public outdoor spaces. The system utilizes a two-stage trajectory selection process where an initial stage of contextual reasoning determines when additional geometric and dynamic evidence is required. By integrating vision-language models with metric geometry and motion data, MOSU2 achieves a mean navigation success rate of 94.2% and condition-detection accuracy of 91.4% in real-world tests involving complex scenarios like construction sites and shared roads, representing an improvement over its single-image predecessor.
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Event evidence and corrections
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Reported quantity · Reported accuracy: 72.2 percent · Basis not reported · Differing source assertions
“g and filters candidate trajectories through multi-modal traversability analysis before selecting a trajectory to move the robot. In real-world experiments spanning stop gestures, construction sites, crosswalks, and roads shared with vehicles, cyclists, and pedestrians, MOSU2 raises mean navigation success from 61.8% to 94.2% and condition-detection accuracy from 72.2% to 91.4% over its single-image predecessor, at a”
Exact source · revision 1Source owner not reported
Reported quantity · Reported accuracy: 91.4 percent · Basis not reported · Differing source assertions
“g and filters candidate trajectories through multi-modal traversability analysis before selecting a trajectory to move the robot. In real-world experiments spanning stop gestures, construction sites, crosswalks, and roads shared with vehicles, cyclists, and pedestrians, MOSU2 raises mean navigation success from 61.8% to 94.2% and condition-detection accuracy from 72.2% to 91.4% over its single-image predecessor, at a”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalMOSU2: Long-range Navigation System with Multi-modal Scene Understanding
This research presents MOSU2, a long-range navigation system for robots operating in public outdoor spaces. The system utilizes a two-stage trajectory selection process where an initial stage of contextual reasoning determines when additional geometric and dynamic evidence is required. By integrating vision-language models with metric geometry and motion data, MOSU2 achieves a mean navigation success rate of 94.2% and condition-detection accuracy of 91.4% in real-world tests involving complex scenarios like construction sites and shared roads, representing an improvement over its single-image predecessor.
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