Collaborative exploration for the construction of visual maps

We examine the problem of learning a visual map of the environment while maintaining an accurate pose estimate. Our approach is based on using two robots in a simple collaborative scheme; in practice, one of these robots can be much less capable than the other. In many mapping contexts, a robot moves about collecting data (images, in particular) which are later used to assemble a map; we can think of map construction as a training process. Without outside information, as a robot collects training images, its position estimate accumulates errors, thus corrupting its knowledge 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 date. In addition to developing a formalism and concept, we validate our results experimentally and present quantitative results demonstrating the performance of the method in over 90 trials.