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Found 7 papers, 0 papers with code
Self-Supervised Traversability Prediction by Learning to Reconstruct Safe Terrain
Typically, this depends on a semantic understanding which is based on supervised learning from images annotated by a human expert.
Early Recall, Late Precision: Multi-Robot Semantic Object Mapping under Operational Constraints in Perceptually-Degraded Environments
Semantic object mapping in uncertain, perceptually degraded environments during long-range multi-robot autonomous exploration tasks such as search-and-rescue is important and challenging.
Copiloting Autonomous Multi-Robot Missions: A Game-inspired Supervisory Control Interface
Real-world deployment of new technology and capabilities can be daunting.
NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge
This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge.
STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation
Although ground robotic autonomy has gained widespread usage in structured and controlled environments, autonomy in unknown and off-road terrain remains a difficult problem.
PLGRIM: Hierarchical Value Learning for Large-scale Exploration in Unknown Environments
In order for an autonomous robot to efficiently explore an unknown environment, it must account for uncertainty in sensor measurements, hazard assessment, localization, and motion execution.
Robotics
Autonomous Off-road Navigation over Extreme Terrains with Perceptually-challenging Conditions
We propose a framework for resilient autonomous navigation in perceptually challenging unknown environments with mobility-stressing elements such as uneven surfaces with rocks and boulders, steep slopes, negative obstacles like cliffs and holes, and narrow passages.
