Real-Time Model-Based SLAM Using Line Segments
Existing monocular vision-based SLAM systems favour interest point features as landmarks, but these are easily occluded and can only be reliably matched over a narrow range of viewpoints. Line segments offer an interesting alternative, as line matching is more stable with respect to viewpoint changes and lines are robust to partial occlusion. In this paper we present a model-based SLAM system that uses 3D line segments as landmarks. Unscented Kalman filters are used to initialise new line segments and generate a 3D wireframe model of the scene that can be tracked with a robust model-based tracking algorithm. Uncertainties in the camera position are fed into the initialisation of new model edges. Results show the system operating in real-time with resilience to partial occlusion. The maps of line segments generated during the SLAM process are physically meaningful and their structure is measured against the true 3D structure of the scene.
KeywordsLine Segment Unscented Kalman Filter Partial Occlusion Edge Feature Model Edge
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- 1.Davison, A.J.: Real-time simultaneous localisation and mapping with a single camera. In: Proc. of the Int. Conf. on Computer Vision (2003)Google Scholar
- 2.Shi, J., Tomasi, C.: Good features to track. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 593–600 (1994)Google Scholar
- 3.Molton, N.D., Davison, A.J., Reid, I.D.: Locally planar patch features for real-time structure from motion. In: Proc. of the British Machine Vision Conference (2004)Google Scholar
- 4.Pupilli, M., Calway, A.: Real-time visual slam with resilience to erratic motion. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (2006)Google Scholar
- 5.Eade, E., Drummond, T.: Scalable monocular slam. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (2006)Google Scholar
- 6.Lemaire, T., Lacroix, S.: Monocular-vision based SLAM using line segments. Technical report, LAAS-CNRS (2006) (submitted to IROS 2006)Google Scholar
- 7.Harris, C.: Tracking with rigid models. Active Vision, 59–73 (1992)Google Scholar
- 8.Armstrong, M., Zisserman, A.: Robust object tracking. In: Proc. of the Asian Conf. on Computer Vision, pp. 58–62 (1995)Google Scholar
- 10.Comport, A.I., Marchand, E., Pressigout, M., Chaumette, F.: Real-time markerless tracking for augmented reality: the virtual visual servoing framework. IEEE Trans. on Visualization and Computer Graphics 12 (2006)Google Scholar
- 12.Comport, A.I., Kragic, D., Marchand, E., Chaumette, F.: Robust real-time visual tracking: Comparison, theoretical analysis and performance evaluation. In: Proc. of the IEEE Int. Conf. on Robotics and Automation, pp. 2852–2857 (2005)Google Scholar
- 13.Julier, S., Uhlmann, J.: A new extension of the kalman filter to nonlinear systems. In: Int. Symp. on Aerospace/Defense Sensing, Simulation and Controls (1997)Google Scholar