Automatic Camera Recovery for Closed or Open Image Sequences
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
High speed obstacle avoidance using monocular vision and reinforcement learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Depth estimation using monocular and stereo cues
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Monocular vision SLAM for indoor aerial vehicles
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
An Efficient Direct Approach to Visual SLAM
IEEE Transactions on Robotics
Discovering Higher Level Structure in Visual SLAM
IEEE Transactions on Robotics
Monocular vision SLAM for indoor aerial vehicles
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Survey of Motion Planning Literature in the Presence of Uncertainty: Considerations for UAV Guidance
Journal of Intelligent and Robotic Systems
Bearing-only visual SLAM for small unmanned aerial vehicles in GPS-denied environments
International Journal of Automation and Computing
| Hi-index | 0.00 |
This paper presents a novel indoor navigation and ranging strategy via monocular camera. By exploiting the architectural orthogonality of the indoor environments, we introduce a new method to estimate range and vehicle states from a monocular camera for vision-based SLAM. The navigation strategy assumes an indoor or indoor-like manmade environment whose layout is previously unknown, GPS-denied, representable via energy based feature points, and straight architectural lines. We experimentally validate the proposed algorithms on a fully self-contained microaerial vehicle (MAV) with sophisticated on-board image processing and SLAM capabilities. Building and enabling such a small aerial vehicle to fly in tight corridors is a significant technological challenge, especially in the absence of GPS signals and with limited sensing options. Experimental results show that the systemis only limited by the capabilities of the camera and environmental entropy.