LAUSR

Visual place recognition is the problem of camera-based localization of a robot given a database of images of known places, potentially under severe appearance changes (e.g., different weather or illumination). Typically, the current query images and the database images are sequences of consecutive images. In previous work, we proposed to adapt hierarchical temporal memory, a biologically plausible model of sequence processing in the human neocortex to address this task. The previous work described the algorithmic steps and showed synthetic experimental results from simulation. This letter extends the approach to application on real-world data based on a novel encoder for state-of-the-art image processing front ends. The neurologically inspired approach is compared with several state-of-the-art algorithms on a variety of datasets and shows preferable performance. Beyond the place recognition performance, the neurological roots of the algorithm result in appealing properties like potentially very energy efficient implementation due to the usage of sparse distributed representations and natural extendability like the integration of motion estimates similar to entorhinal grid cells. Finally, we underline its practical applicability by online, soft real-time application on a mobile robot.