Chapter 1 Cooperative Exploration Localization and Visual Map Construction
We examine the problem of learning a visual map of the environment based on discrete landmarks. While making this map we seek to maintain an accurate pose estimate for the mapping robots. Our approach is based on using a team of at least two (heterogeneous) mobile robots in a simple collaborative scheme. In many mapping contexts, a robot moves about the environment collecting data (images, in particular) which are later used to assemble a map; we view the map construction as both a knowledge acquisition and a training process. Without reference to the environment, as a robot collects training images, its position estimate accumulates errors, thus corrupting its estimate of the positions from which observations are taken. We address this problem by deploying a second robot to observe the first one as it explores, thereby establishing a virtual tether, and enabling an accurate estimate of the robot's position while it constructs the map. We refer to this process as cooperative localization. The images collected during this process are assembled into a representation that allows vision based position estimation from a single image at a later time. In addition to developing a formalism and concept, we validate our approach experimentally and present quantitative results demonstrating the performance of the method in over 90 trials.