Robotic Mapping Using Measurement Likelihood Filtering

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.