Abstract
This work presents an improved visual odometry using omnidirectional images. The main purpose is to generate a reliable prior input which enhances the SLAM (Simultaneous Localization and Mapping) estimation tasks within the framework of navigation in mobile robotics, in detriment of the internal odometry data. Generally, standard SLAM approaches extensively use data such as the main prior input to localize the robot. They also tend to consider sensory data acquired with GPSs, lasers or digital cameras, as the more commonly acknowledged to re-estimate the solution. Nonetheless, the modeling of the main prior is crucial, and sometimes especially challenging when it comes to non-systematic terms, such as those associated with the internal odometer, which ultimately turn to be considerably injurious and compromise the convergence of the system. This omnidirectional odometry relies on an adaptive feature point matching through the propagation of the current uncertainty of the system. Ultimately, it is fused as the main prior input in an EKF (Extended Kalman Filter) view-based SLAM system, together with the adaption of the epipolar constraint to the omnidirectional geometry. Several improvements have been added to the initial visual odometry proposal so as to produce better performance. We present real data experiments to test the validity of the proposal and to demonstrate its benefits, in contrast to the internal odometry. Furthermore, SLAM results are included to assess its robustness and accuracy when using the proposed prior omnidirectional odometry.
Highlights
In the field of mobile robotics the problem of SLAM entails a demanding task which requires the simultaneous accomplishment of map building and robot estimation
The equipment used for the acquisition of data has already been presented by Figure 1a. It consists of a Pioneer P3-AT robot which is mounted with an omnidirectional camera, internal odometer and laser range finder, which produces a general ground truth [44,45] for comparison tasks
Once validated the suitability of the omnidirectional visual odometry to produce reliable and efficient results, we can move forward in order to test the behaviour of a SLAM application when the internal odometry is substituted by the proposed omnidirectional approach as the main prior input to the system
Summary
In the field of mobile robotics the problem of SLAM entails a demanding task which requires the simultaneous accomplishment of map building and robot estimation. This aspect poses a challenge when it comes to the complexity associated to the incremental nature of the process. In this context, the presence of non-linearities induces undesired injurious effects that may gravely aggravate and jeopardize the final estimation. The presence of non-linearities induces undesired injurious effects that may gravely aggravate and jeopardize the final estimation In this sense, the internal odometer of the vehicle may be considered as a problematic source of non-linear noise [1]. Using the odometry data as a first prior input implies extra expenses for the system in order to obtain and maintain the convergence of the final estimation [2].
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