---
title: "Learning Topological Maps with Weak Local Odometric Information"
authors:
  - "Hagit Shatkay"
  - "Leslie Pack Kaelbling"
doi: ""
url: "https://ijcai.org/proceedings/1997/133"
pdf: "https://ijcai.org/proceedings/1997/0133.pdf"
venue: "Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence"
year: 1997
pages: "920-928"
track: "Learning"
keywords:
---

# Learning Topological Maps with Weak Local Odometric Information

Hagit Shatkay, Leslie Pack Kaelbling

*Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence*, pages 920-928.

## 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.