Tracking multiple targets from a mobile robot platform using a laser range scanner

Abstract

A major issue in the field of mobile robotics today is the detection and tracking of moving objects (DATMO) from a moving observer. In dynamic and highly populated environments, this problem presents a complex and computationally demanding task. It can be divided in subproblems such as robot's relative motion compensation, feature extraction, measurement clustering, data association and targets' state vector estimation. In this paper we present an innovative approach that addresses all these issues exploiting various probabilistic and deterministic techniques. The algorithm utilizes real laser-scanner data to dynamically extract moving objects from their background environment, using a time-fading grid map method, and tracks the identified targets employing a Joint Probabilistic Data Association with Interacting Multiple Model (JPDA-IMM) algorithm. The resulting technique presents a computationally efficient approach to already existing target-tracking research for real time application scenarios.

2D Spatial Keystone Transform for Sub-Pixel Motion Extraction from Noisy Occupancy Grid Map

“…Efficient perception of and reasoning about environments is still a major challenge for mobile robots operating in dynamic, densely cluttered or highly populated environments [1]- [3], which usually includes three interleaved tasks, i.e., Simultaneous Localization And Mapping (SLAM) [4], [5], Detection And Tracking of Moving Objects (DATMO) [6] and Cell Transition Mapping (CTM) [7].…”