A stochastic map for uncertain spatial relationships
Proceedings of the 4th international symposium on Robotics Research
Improved Occupancy Grids for Map Building
Autonomous Robots
Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans
Journal of Intelligent and Robotic Systems
Occupancy grids: a probabilistic framework for robot perception and navigation
Occupancy grids: a probabilistic framework for robot perception and navigation
Learning Occupancy Grid Maps with Forward Sensor Models
Autonomous Robots
Including probabilistic target detection attributes into map representations
Robotics and Autonomous Systems
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Toward multidimensional assignment data association in robot localization and mapping
IEEE Transactions on Robotics
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters
IEEE Transactions on Robotics
Target tracking without line of sight using range from radio
Autonomous Robots
Laser and Radar Based Robotic Perception
Foundations and Trends in Robotics
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The classical occupancy grid formulation requires the use of a priori known measurement likelihoods whose values are typically either assumed or learned from training data. Furthermore, in previous approaches, the likelihoods used to propagate the occupancy map variables are, in fact, independent of the state of interest and are derived from the spatial uncertainty of the detected point. This allows the use of a discrete Bayes filter as a solution to the problem, as discrete occupancy measurement likelihoods are used. In this paper, we first shown that once the measurement space is redefined, theoretically accurate and state-dependant measurement likelihoods can be obtained and used in the propagation of the occupancy random variable. The required measurement likelihoods for occupancy filtering are, in fact, those commonly encountered in both the landmark detection and data association hypotheses decisions. However, the required likelihoods are generally a priori unknown as they are a highly non-linear function of the landmark's signal-to-noise ratio and the surrounding environment. The probabilistic occupancy mapping problem is therefore reformulated as a continuous joint estimation problem where the measurement likelihoods are treated as continuous random states which must be jointly estimated with the map. In particular, this work explicitly considers the sensors detection and false-alarm probabilities in the occupancy mapping formulation. A particle solution is proposed which recursively estimates both the posterior on the map and the measurement likelihoods. The ideas presented in this paper are demonstrated in the field robotics domain using a millimeter wave radar sensor and comparisons with previous approaches, using constant discrete measurement likelihoods, are shown. A manually constructed ground-truth map and satellite imagery are also provided for map validation.