A computational approach for corner and vertex detection
International Journal of Computer Vision
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Preemptive RANSAC for Live Structure and Motion Estimation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Stereo-Based Ego-Motion Estimation Using Pixel Tracking and Iterative Closest Point
ICVS '06 Proceedings of the Fourth IEEE International Conference on Computer Vision Systems
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
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In this paper different algorithms for visual odometry are evaluated for navigating an agricultural weeding robot in outdoor field environment. Today there is an encoder wheel that keeps track of the weeding tools position relative the camera, but the system suffers from wheel slippage and errors caused by the uneven terrain. To overcome these difficulties the aim is to replace the encoders with visual odometry using the plant recognition camera. Four different optical flow algorithms are tested on four different surfaces, indoor carpet, outdoor asphalt, grass and soil. The tests are performed on an experimental platform. The result shows that the errors consist mainly of dropouts caused by overriding maximum speed, and of calibration error due to uneven ground. The number of dropouts can be reduced by limiting the maximum speed and detection of missing frames. The calibration problem can be solved using stereo cameras. This gives a height measurement and the calibration will be given by camera mounting. The algorithm using normalized cross-correlation shows the best result concerning number of dropouts, accuracy and calculation time.