A Distributed Sensing Approach for Single Platform Image-Based Localization

We present a distributed image based robot (re)-localization system with four non-stereo monocular cameras using a deep Convolutional Network (Convnet). Our system trains the well known Posenet (Kendall et al 2015) CNN architecture, with minor changes, for regressing the position of a ground robot using a compound image, consisting of images from four non-stereo monocular cameras mounted on the robotic platform. The training of the network is done end-to-end without the need for any special feature engineering to handle the compound image input. Our results show that there is significant advantage to using a compound image obtained from multiple cameras with non-overlapping field of view (non-stereo) as compared to using images from single cameras for image based localization. The compound-image based training yields median accuracy of 12 cm in an indoor environment which is at least twice as good as the results obtained using the same network trained on monocular image inputs.