{"type":"ScholarlyArticle","title":"Learning Topological Maps with Weak Local Odometric Information","authors":[{"name":"Hagit Shatkay","slug":"hagit-shatkay","given":"Hagit","family":"Shatkay","affiliation":""},{"name":"Leslie Pack Kaelbling","slug":"leslie-pack-kaelbling","given":"Leslie Pack","family":"Kaelbling","affiliation":""}],"abstract":"Topological maps provide a useful abstraction for robotic navigation and planning. Although stochastic maps can theoretically be learned using the Baum- Welch algorithm, without strong prior constraint on the structure of the model it is slow to converge, requires a great deal of data, and is often stuck in local minima. In this paper, we consider a special case of hidden Markov models for robot-navigation environments, in which states are associated with points in a metric configuration space. We assume that the robot has some odometric ability to measure relative transformations between its configurations. Such odometry is typically not precise enough to suffice for building a global map, but it does give valuable local information about relations between adjacent states. We present an extension of the Baum- Welch algorithm that takes advantage of this local adometric information, yielding faster convergence to better solutions with less data.","doi":null,"url":"https://ijcai.org/proceedings/1997/133","pdf":"https://ijcai.org/proceedings/1997/0133.pdf","markdown":"https://ijcai.org/proceedings/1997/133.md","bibtex":"https://ijcai.org/proceedings/1997/bibtex/133","volume":{"slug":"1997","title":"Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1997","url":"https://ijcai.org/proceedings/1997/","year":1997,"location":"Nagoya, Japan","dates":"","editors":"Martha E. Pollack","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Learning","track":"Learning","number":133,"pages":{"first":920,"last":928},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}