A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vision for Mobile Robot Navigation: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
An Invitation to 3-D Vision: From Images to Geometric Models
An Invitation to 3-D Vision: From Images to Geometric Models
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Vision-Based SLAM: Stereo and Monocular Approaches
International Journal of Computer Vision
Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab
Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Fusing Monocular Information in Multicamera SLAM
IEEE Transactions on Robotics
Large-Scale 6-DOF SLAM With Stereo-in-Hand
IEEE Transactions on Robotics
Hi-index | 0.00 |
In this work, we examine the classic problem of robot navigation via visual simultaneous localization and mapping (SLAM), but introducing the concept of dual optical and thermal (cross-spectral) sensing with the addition of sensor handover from one to the other. In our approach we use a novel combination of two primary sensors: co-registered optical and thermal cameras. Mobile robot navigation is driven by two simultaneous camera images from the environment over which feature points are extracted and matched between successive frames. A bearing-only visual SLAM approach is then implemented using successive feature point observations to identify and track environment landmarks using an extended Kalman filter (EKF). Six-degree-of-freedom mobile robot and environment landmark positions are managed by the EKF approach illustrated using optical, thermal and combined optical/thermal features in addition to handover from one sensor to another. Sensor handover is primarily targeted at a continuous SLAM operation during varying illumination conditions (e.g., changing from night to day). The final methodology is tested in outdoor environments with variation in the light conditions and robot trajectories producing results that illustrate that the additional use of a thermal sensor improves the accuracy of landmark detection and that the sensor handover is viable for solving the SLAM problem using this sensor combination.