On the representation and estimation of spatial uncertainly
International Journal of Robotics Research
Coordination for Multi-Robot Exploration and Mapping
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Algorithms for Matching 3D Line Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distributed Cooperative Outdoor Multirobot Localization and Mapping
Autonomous Robots
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Multi-robot Simultaneous Localization and Mapping using Particle Filters
International Journal of Robotics Research
Predicting the Performance of Cooperative Simultaneous Localization and Mapping (C-SLAM)
International Journal of Robotics Research
Vision-Based SLAM: Stereo and Monocular Approaches
International Journal of Computer Vision
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
International Journal of Robotics Research
Subjective local maps for hybrid metric-topological SLAM
Robotics and Autonomous Systems
Parallel Tracking and Mapping for Small AR Workspaces
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Structure-from-motion using lines: Representation, triangulation, and bundle adjustment
Computer Vision and Image Understanding
Finding good cycle constraints for large scale multi-robot SLAM
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Consistent cooperative localization
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
SLAM in O(log n) with the combined Kalman - information filter
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Event-driven loop closure in multi-robot mapping
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Undelayed initialization of line segments in monocular SLAM
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Real-time model-based SLAM using line segments
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Hierarchical SLAM: Real-Time Accurate Mapping of Large Environments
IEEE Transactions on Robotics
Exactly Sparse Delayed-State Filters for View-Based SLAM
IEEE Transactions on Robotics
Fusing Monocular Information in Multicamera SLAM
IEEE Transactions on Robotics
Inverse Depth Parametrization for Monocular SLAM
IEEE Transactions on Robotics
Cooperative SLAM using M-Space representation of linear features
Robotics and Autonomous Systems
Hi-index | 0.00 |
This paper addresses the cooperative localization and visual mapping problem with multiple heterogeneous robots. The approach is designed to deal with the challenging large semi-structured outdoors environments in which aerial/ground ensembles are to evolve. We propose the use of heterogeneous visual landmarks, points and line segments, to achieve effective cooperation in such environments. A large-scale SLAM algorithm is generalized to handle multiple robots, in which a global graph maintains the relative relationships between a series of local sub-maps built by the different robots. The key issue when dealing with multiple robots is to find the link between them, and to integrate these relations to maintain the overall geometric consistency; the events that introduce these links on the global graph are described in detail. Monocular cameras are considered as the primary extereoceptive sensor. In order to achieve the undelayed initialization required by the bearing-only observations, the well-known inverse-depth parametrization is adopted to estimate 3D points. Similarly, to estimate 3D line segments, we present a novel parametrization based on anchored Plucker coordinates, to which extensible endpoints are added. Extensive simulations show the proposed developments, and the overall approach is illustrated using real-data taken with a helicopter and a ground rover.