Part of Advances in Neural Information Processing Systems 18 (NIPS 2005)
Brad Schumitsch, Sebastian Thrun, Gary Bradski, Kunle Olukotun
This paper presents a new filter for online data association problems in high-dimensional spaces. The key innovation is a representation of the data association posterior in information form, in which the “proxim- ity” of objects and tracks are expressed by numerical links. Updating these links requires linear time, compared to exponential time required for computing the exact posterior probabilities. The paper derives the algorithm formally and provides comparative results using data obtained by a real-world camera array and by a large-scale sensor network simu- lation.