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SAFARI Risk-Aware Autonomous Exploration Framework Released

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Ugur Kafkasyali, Mihnea Toader and others propose the SAFARI risk-aware semantic exploration framework, which treats object detection as a native channel of a unified probabilistic map, allowing geometric and semantic evidence to jointly drive exploration and planning. The authors report that on a quadruped platform across three field sites, 11 missions were completed, covering on average 98.7% of the reachable mission area, with 97.6% of mission time requiring no operator intervention, and the ability to detect objects with feature sizes as small as 15 cm. Ablation experiments show that object size awareness and safety-aware path planning are crucial for hazardous object search.

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  1. SAFARI: Semantic- and Frontier-Driven Autonomous Risk Inspection Framework

    SAFARI treats object detection as a native channel of a unified probabilistic map, letting geometric and semantic evidence jointly drive exploration and planning. The authors completed 11 missions on a quadruped platform across three field sites, achieving an average coverage of 98.7% of the accessible mission area, with 97.6% of mission time requiring no operator intervention, and detecting objects with characteristic dimensions as small as 15 cm. Ablation experiments show that object-size awareness and safety-aware path planning are critical for hazardous-object search.

    arXiv cs.RO ·

Related topics

Perception and localizationPlanning and decision makingSafety and reliabilityResearch papers

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