In this paper we present a new real-time hierarchical (topological/metric) Visual SLAM system focusing on the localization of a vehicle in large-scale outdoor urban environments. It is exclusively based on the visual information provided by both a low-cost wide-angle stereo camera and a low-cost GPS. Our approach divides the whole map into local sub-maps identified by the so-called fingerprint (reference poses). At the sub-map level (low level SLAM), 3D sequential mapping of natural landmarks and the vehicle location/orientation are obtained using a top-down Bayesian method to model the dynamic behavior. A higher topological level (high level SLAM) based on references poses has been added to reduce the global accumulated drift, keeping real-time constraints. Using this hierarchical strategy, we keep local consistency of the metric sub-maps, by mean of the EKF, and global consistency by using the topological map and the MultiLevel Relaxation (MLR) algorithm. GPS measurements are integrated at both levels, improving global estimation. Some experimental results for different large-scale urban environments are presented, showing an almost constant processing time.

This paper presents a new real-time hierarchical (topological/metric) simultaneous localization and mapping (SLAM) system. It can be applied to the robust localization of a vehicle in large-scale outdoor urban environments, improving the current vehicle navigation systems, most of which are only based on Global Positioning System (GPS). Then, it can be used on autonomous vehicle guidance with recurrent trajectories (bus journeys, theme park internal journeys, etc.). It is exclusively based on the information provided by both a low-cost, wide-angle stereo camera and a low-cost GPS. Our approach divides the whole map into local submaps identified by the so-called fingerprints (vehicle poses). In this submap level (low-level SLAM), a metric approach is carried out. There, a 3-D sequential mapping of visual natural landmarks and the vehicle location/orientation are obtained using a top-down Bayesian method to model the dynamic behavior. GPS measurements are integrated within this low-level improving vehicle positioning. A higher topological level (high-level SLAM) based on fingerprints and the multilevel relaxation (MLR) algorithm has been added to reduce the global error within the map, keeping real-time constraints. This level provides nearly consistent estimation, keeping a small degradation with GPS unavailability. Some experimental results for large-scale outdoor urban environments are presented, showing an almost constant processing time.

This paper presents a new method for real-time SLAM calculation applied to autonomous robot navigation in large environments without restrictions. It is exclusively based on the information provided by a cheap wide-angle stereo camera. Our approach divide the global map into local sub- maps identified by the so-called SIFT fingerprint. At the sub- map level (low level SLAM), 3D sequential mapping of natural land-marks and the robot location/orientation are obtained using a top-down Bayesian method to model the dynamic behavior. A high abstraction level to reduce the global accumulated drift, keeping real-time constraints, has been added (high level SLAM). This uses a SIFT correction method based on the sub-maps' fingerprints. A comparison of the low SLAM level using our method and SIFT features has been carried out. Some experimental results using a real large environment are presented.

This paper presents a new method for real-time ego-motion calculation applied to the location/orientation of a cheap wide-angle stereo camera in a 3D environment. The objective is to apply it to a mobile robot navigation system. To achieve that, the goal is to solve the simultaneous localization and mapping (SLAM) problem. Our approach consists in the 3D sequential mapping of natural landmarks by means of a stereo camera, which also provides means to obtain the camera location/orientation. The dynamic behavior is modeled using a top-down Bayesian method. The results show a comparison between our system and a monocular visual SLAM system using a hand-waved camera. Several improvements related to no priori environment knowledge requirements, lower processing time (real-time constrained) and higher robustness is presented