2015/12/01 by Lawson L. S. Wong, Thanard Kurutach, Wong, Lawson L. S. +6
Computer Science · Medicine · #Artificial Intelligence (cs.AI) #Bayesian Methods and Mixture Models #Data-Driven Disease Surveillance #FOS: Computer and information sciences #Machine Learning (cs.LG) #Robotics (cs.RO) #Target Tracking and Data Fusion in Sensor Networks #cs.AI #cs.LG #cs.RO
paper · pdf · doi:10.48550/arxiv.1512.00573
openalex publication_date 2015/12/01 · arxiv created 2015/12/02 · arxiv updated 2015/12/03 · openalex created_date 2019/07/30 · openalex updated_date 2026/07/28
To accomplish tasks in human-centric indoor environments, robots need to represent and understand the world in terms of objects and their attributes. We refer to this attribute-based representation as a world model, and consider how to acquire it via noisy perception and maintain it over time, as objects are added, changed, and removed in the world. Previous work has framed this as multiple-target tracking problem, where objects are potentially in motion at all times. Although this approach is general, it is computationally expensive. We argue that such generality is not needed in typical world modeling tasks, where objects only change state occasionally. More efficient approaches are enabled by restricting ourselves to such semi-static environments. We consider a previously-proposed clustering-based world modeling approach that assumed static environments, and extend it to semi-static domains by applying a dependent Dirichlet-process (DDP) mixture model. We derive a novel MAP inference algorithm under this model, subject to data association constraints. We demonstrate our approach improves computational performance in semi-static environments.