A fully decentralized multi-sensor system for tracking and surveillance
International Journal of Robotics Research
Multi-sensor fusion: fundamentals and applications with software
Multi-sensor fusion: fundamentals and applications with software
Artificial intelligence and mobile robots: case studies of successful robot systems
Artificial intelligence and mobile robots: case studies of successful robot systems
Self-stabilizing systems in spite of distributed control
Communications of the ACM
RUR '95 Proceedings of the International Workshop on Reasoning with Uncertainty in Robotics
A new fault-tolerant algorithm for clock synchronization
PODC '84 Proceedings of the third annual ACM symposium on Principles of distributed computing
Learning Occupancy Grid Maps with Forward Sensor Models
Autonomous Robots
Biological and cognitive foundations of intelligent sensor fusion
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Global-referenced navigation grids for off-road vehicles and environments
Robotics and Autonomous Systems
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Measurements from sensors as they are used for robotic grid map applications typically show behavior like degradation or discalibration over time, which affects the quality of the generated maps. This paper presents two novel algorithms for the generation of certainty grids dealing with this behavior. The first algorithm named Fault-Tolerant Certainty Grid (FTCG) performs voting over multiple sensor readings. This approach removes up to (n-1)/2 faulty measurements for grid cells that are updated by n independent sensors, however it requires that each grid cell is covered by at least three different independent sensors. The second algorithm named Robust Certainty Grid (RCG) uses a sensor validation method that detects abnormal sensor measurements and adjusts a confidence value for each sensor. This method also supports reintegration of recovered sensors from transient faults and sensor maintenance by providing a measurement for the operability of a sensor. The RCG algorithm works with at least three sensors with a partially overlapping sensing range and needs fewer sensor inputs and less memory than the FTCG approach. Results from simulation and an experimental evaluation on an autonomous mobile robot show that under the presence of unreliable sensor data, both algorithms perform better than the Bayesian approach typically used for certainty grids.