A Visual Attention-Based Approach for Automatic Landmark Selection and Recognition

Visual attention refers to the ability of a vision system to rapidly detect visually salient locations in a given scene. On the other hand, the selection of robust visual landmarks of an environment represents a cornerstone of reliable vision-based robot navigation systems. Indeed, can salient scene locations provided by visual attention be useful for robot navigation? This work investigates the potential and effectiveness of the visual attention mechanism to provide pre-attentive scene information to a robot navigation system. The basic idea is to detect and track the salient locations, or spots of attention by building trajectories that memorize the spatial and temporal evolution of these spots. Then, a persistency test, which is based on the examination of the lengths of built trajectories, allows the selection of good environment landmarks. The selected landmarks can be used for feature-based localization and mapping systems which helps mobile robot to accomplish navigation tasks.