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This paper introduces a novel perception framework that has the ability toidentify and track objects in autonomous vehicle's field of view. The proposedalgorithms don't require any training for achieving this goal. The frameworkmakes use of ego-vehicle's pose estimation and a KD-Tree-based segmentationalgorithm to generate object clusters. In turn, using a VFH technique, thegeometry of each identified object cluster is translated into a multi-modal PDFand a motion model is initiated with every new object cluster for the purposeof robust spatio-temporal tracking. The methodology further uses statisticalproperties of high-dimensional probability density functions and Bayesianmotion model estimates to identify and track objects from frame to frame. Theeffectiveness of the methodology is tested on a KITTI dataset. The results showthat the median tracking accuracy is around 91% with an end-to-endcomputational time of 153 milliseconds

Novel Perception Algorithmic Framework For Object Identification and Tracking In Autonomous Navigation