Visual Odometric Navigation: the Generalized Feature Vector way

One of the main challenges faced by object tracking and environment-modeling techniques is the frame-to-frame correspondence of the object of interest. False detections may lead to the tracking of wrong object thus misrepresenting information about the object location and its track. The tracking algorithm of the detected object should also be computationally inexpensive and suitable for real time applications. This paper discusses how GFV, a multidimensional entity encapsulating multiple feature parameters, can uniquely identify dominant features of an object, and increase the detection reliability due to its potential to function consistently in any kind of environment, uninfluenced by view point invariance or extrinsic factors, thus generating minimal false alarms. Further a method to determine the 3D position of the object is presented which works on uncalibrated camera images and can be successfully applied to online processes. Experimental analysis using a outdoor mobile robot have been carried out to establish the competence of the algorithm. A statistical approach to reject outlier data, if any, is applied while generating the trajectory of the mobile robot used for experiments