Modeling Detection Statistics in Feature‐Based Robotic Navigation for Range Sensors

This paper proposes using the number of range measurements that a detector utilizes to generate a detection as its descriptor. This one dimensional descriptor can be calculated with many range-based detectors, and its expected value is used to derive detection statistics which take into account feature occlusions to improve robotic navigation performance. To demonstrate the advantages of estimating detection statistics, they are estimated and tested within Random Finite Set and vector-based Simultaneous Localization and Mapping (SLAM) algorithms. Results from simulations and real experiments demonstrate the advantages of explicitly modeling feature detection statistics in both frameworks.