2D spherical spaces for objects recognition under harsh lighting conditions

For an object recognition task in an unknown environment, we propose a novel approach for illumination recovery of surface with cast shadows and specularities by using the object spherical spaces properties. Robust objects recognition in complex environment is fundamental to robot intelligence and manipulation. The proposed method is done for reducing the illumination effects on the objects detection and recognition processes. In this work, objects reference images are regenerated to match the scene lighting environment to increase the success rate of the recognition process. First, a database is generated by computing the albedo and surface normals from captured 2D images of the target objects. Next, the scene lighting direction and illumination coefficients are estimated. Finally, by using the calculated spherical spaces properties we regenerate objects reference data to match the search area illumination condition. In this work, practical real time processing speed and small image size were considered when designing the framework. In contrast to other techniques, our work requires no 3D models for the objects training process and takes images from a single camera as an input. Using our proposed 2D Spherical Spaces experimentally showed noticeable improvements in an objects identification task performed by an autonomous robot in a harshly illuminated environment.