This presentation will address the issues of position estimation in the context of mobile robotics. Our current work involves several approaches to this problem including the use of discrete control and an analysis of computation complexity.
Traditional methods use geometric scene models.
A common methodology combines noisy sensor data with unreliable odometry using a Kalman filter. I will briefly consider this issue and present an extension to the standard framework that involves the use of a multi-scale gating function. To avoid the need for a geometric scene model, I will then consider appearance-based position estimation. By using raw sensor images, from either sonar or video, we can accurately estimate the position of our robot with only a very coarse initial position estimate. This is accomplished by storing an ensemble of highly compressed images which capture the variations in appearance of the environment.