Incremental sampling based algorithms for state estimation
Pratik Chaudhari · 2012
Perception is a crucial aspect of the operation of autonomous vehicles. With a multitude of different sources of sensor data, it becomes important to have algorithms which can process the available information quickly and provide a timely solution. Also, an inherently contin-uous world is sensed by robot sensors and converted into discrete packets of information. Algorithms that can take advantage of this setup, i.e., which have a sound founding in continuous time formulations but which can effectively discretize the available information in an incremental manner according to different requirements can potentially outperform conventional perception frameworks. Inspired from recent results in motion planning algo-rithms, this thesis aims to address these two aspects of the problem of robot perception, through novel incremental and anytime algorithms. The first part of the thesis deals with algorithms for different estimation problems, such as filtering, smoothing, and trajectory decoding. They share the basic idea that a