A framework for vision based bearing only 3D SLAM
Citations Over TimeTop 1% of 2006 papers
Abstract
This paper presents a framework for 3D vision based bearing only SLAM using a single camera, an interesting setup for many real applications due to its low cost. The focus in is on the management of the features to achieve real-time performance in extraction, matching and loop detection. For matching image features to map landmarks a modified, rotationally variant SIFT descriptor is used in combination with a Harris-Laplace detector. To reduce the complexity in the map estimation while maintaining matching performance only a few, high quality, image features are used for map landmarks. The rest of the features are used for matching. The framework has been combined with an EKF implementation for SLAM. Experiments performed in indoor environments are presented. These experiments demonstrate the validity and effectiveness of the approach. In particular they show how the robot is able to successfully match current image features to the map when revisiting an area
Related Papers
- → A MapReduce Approach for SIFT Feature Extraction(2013)4 cited
- A Technical Analysis of Image StitchingAlgorithm Using Different Corner DetectionMethods(2015)
- → An Improved SIFT Algorithm Based on FAST Corner Detection(2013)1 cited
- → Comparative Study of Corner and Feature Extractors for Real-Time Object Recognition in Image Processing(2014)1 cited
- Pattern Recognition By a Scaled Corners Detection(2013)