A Four-step Camera Calibration Procedure with Implicit Image Correction
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Real-Time Simultaneous Localisation and Mapping with a Single Camera
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Fastslam: a factored solution to the simultaneous localization and mapping problem with unknown data association
Real-time Localization in Outdoor Environments using Stereo Vision and Inexpensive GPS
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing
International Journal of Robotics Research
A multilevel relaxation algorithm for simultaneous localization and mapping
IEEE Transactions on Robotics
Large-Scale 6-DOF SLAM With Stereo-in-Hand
IEEE Transactions on Robotics
High-Integrity IMM-EKF-Based Road Vehicle Navigation With Low-Cost GPS/SBAS/INS
IEEE Transactions on Intelligent Transportation Systems
An Efficient Approach to Onboard Stereo Vision System Pose Estimation
IEEE Transactions on Intelligent Transportation Systems
Optimized EIF-SLAM algorithm for precision agriculture mapping based on stems detection
Computers and Electronics in Agriculture
International Journal of Robotics Research
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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.