Tracking and data association
Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
Shape registration using optimization for mobile robot navigation
Shape registration using optimization for mobile robot navigation
Propagating Covariance in Computer Vision
Proceedings of the Theoretical Foundations of Computer Vision, TFCV on Performance Characterization in Computer Vision
Robotics and Autonomous Systems
Towards High-resolution Imaging from Underwater Vehicles
International Journal of Robotics Research
Underwater SLAM in man-made structured environments
Journal of Field Robotics
Journal of Field Robotics - Three-Dimensional Mapping, Part 3
SLAM Estimation in Dynamic Outdoor Environments: A Review
ICIRA '09 Proceedings of the 2nd International Conference on Intelligent Robotics and Applications
An efficient approach to bathymetric SLAM
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Journal of Field Robotics - Visual Mapping and Navigation Outdoors
Spectral registration of noisy sonar data for underwater 3D mapping
Autonomous Robots
Hierarchical SLAM: Real-Time Accurate Mapping of Large Environments
IEEE Transactions on Robotics
Efficient View-Based SLAM Using Visual Loop Closures
IEEE Transactions on Robotics
Navigation Technologies for Autonomous Underwater Vehicles
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Hi-index | 0.00 |
This paper proposes a pose-based algorithm to solve the full simultaneous localization and mapping problem for autonomous underwater vehicle (AUV) navigating in unknown and possibly unstructured environments. The proposed method first estimates the local path traveled by the robot while forming the acoustic image (scan) with range data coming from a mono-beam rotating sonar head, providing position estimates for correcting the distortions that the vehicle motion produces in the scans. Then, consecutive scans are cross-registered under a probabilistic scan matching technique for estimating the displacements of the vehicle including the uncertainty of the scan matching result. Finally, an augmented state extended Kalman filter estimates and keeps the registered scans poses. No prior structural information or initial pose are considered. The viability of the proposed approach has been tested reconstructing the trajectory of a guided AUV operating along a 600 m path within a marina environment.