Real-Time Simultaneous Localisation and Mapping with a Single Camera
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Hierarchical SLAM: Real-Time Accurate Mapping of Large Environments
IEEE Transactions on Robotics
Effective maximum likelihood grid map withconflict evaluation filter using sonar sensors
IEEE Transactions on Robotics
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Sensor Fusion for SLAM Based on Information Theory
Journal of Intelligent and Robotic Systems
Enhanced mapping of multi-robot using distortion reducing filter based SIFT
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Local map-based exploration for mobile robots
Intelligent Service Robotics
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Improving the practical capability of SLAM requires effective sensor fusion to cope with the large uncertainties from the sensors and environment. Fusing ultrasonic and vision sensors possesses advantages of both economical efficiency and complementary cooperation. In particular, it can resolve the false data association and divergence problem of an ultrasonic sensor-only algorithm and overcome both the low frequency of SLAM update caused by the computational burden and the weakness to illumination changes of a vision sensor-only algorithm. In this paper, we propose a VR-SLAM (Vision and Range sensor-SLAM) algorithm to combine ultrasonic sensors and stereo camera very effectively. It consists of two schemes: (1) extracting robust point and line features from sonar data and (2) recognizing planar visual objects using a multi-scale Harris corner detector and its SIFT descriptor from a pre-constructed object database. We show that fusing these schemes through EKF-SLAM frameworks can achieve correct data association via the object recognition and high frequency update via the sonar features. The performance of the proposed algorithm was verified by experiments in various real indoor environments.