In this article we present an algorithm to estimate the orientation of a robot relative to an orientation specified at the beginning of the process. This is done by computing the rotation of the robot between successive panoramic images, grabbed on the robot while it moves, using a subsymbolic method to match the images. The context of the work is simultaneous localization and mapping ͑SLAM͒ in unstructured and unmodified environments. As such, very few assumptions are made about the environment and the robot's displacement. The algorithm's performance depends on the value of a number of parameters being determined to provide overall good performance of the system. The performance is evaluated in different situations ͑trajectories and environments͒ with the same parameters and the results show that the method performs adequately for its intended use. In particular, the error is shown to be drifting slowly, in fact much slower than unprocessed inertial sensors, thus only requiring infrequent realignment, for example, when relocalizing in a topological map. Limitations of the proposed methods are also shown and discussed. Figure 23. CARPARK: error between the magnetic and visual orientations.

Labrosse, F. (2007). Short and long-range visual navigation using warped panoramic images. Robotics and Autonomous Systems, 55 (9), 675-684.In this paper, we present a method that uses panoramic images to perform long-range navigation as a succession of short-range homing steps along a route specified by appearances of the environment of the robot along the route. Our method is different from others in that it does not extract any features from the images and only performs simple image processing operations. The method does only make weak assumptions about the surroundings of the robot, assumptions that are discussed. Furthermore, the method uses a technique borrowed from computer graphics to simulate the effect in the images of short translations of the robot to compute local motion parameters. Finally, the proposed method shows that it is possible to perform navigation without explicitly knowing where the destination is nor where the robot currently is. Results in our Lab are presented that show the performance of the proposed system.Peer reviewe

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