Appearance-Based SLAM Relying on a Hybrid Laser/Omnidirectional Sensor

International audienceThis paper describes an efficient hybrid laser/vision appearance-based approach to provide a mobile robot with rich 3D information about its environment. By combining the information from an omnidirectional camera and a laser range finder, reliable 3D positioning and an accurate 3D representation of the environment is obtained subject to illumination changes even in the presence of occluding and moving objects. A scan matching technique is used to initialize the tracking algorithm in order to ensure rapid convergence and reduce computational cost. The proposed method is validated in an indoor environment using data taken from a mobile robot equipped with a 2D laser range finder and an omnidirectional camera

Visual sequence-based place recognition for changing conditions and varied viewpoints

Correctly identifying previously-visited locations is essential for robotic place recognition and localisation. This thesis presents training-free solutions to vision-based place recognition under changing environmental conditions and camera viewpoints. Using vision as a primary sensor, the proposed approaches combine image segmentation and rescaling techniques over sequences of visual imagery to enable successful place recognition over a range of challenging environments where prior techniques have failed