Rao-Blackwellized Particle Filtering for Mapping Dynamic Environments
ABSTRACT A general method for mapping dynamic environments using a Rao-Blackwellized particle filter is presented. The algorithm rigorously addresses both data association and target tracking in a single unified estimator. The algorithm relies on a Bayesian factorization to separate the posterior into: 1) a data association problem solved via particle filter; and 2) a tracking problem with known data associations solved by Kalman filters developed specifically for the ground robot environment. The algorithm is demonstrated in simulation and validated in the real world with laser range data, showing its practical applicability in simultaneously resolving data association ambiguities and tracking moving objects.
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ABSTRACT: The development of autonomous vehicles for urban driving has seen rapid progress in the past 30 years. This paper provides a summary of the current state of the art in autonomous driving in urban environments, based primarily on the experiences of the authors in the 2007 DARPA Urban Challenge (DUC). The paper briefly summarizes the approaches that different teams used in the DUC, with the goal of describing some of the challenges that the teams faced in driving in urban environments. The paper also highlights the long-term research challenges that must be overcome in order to enable autonomous driving and points to opportunities for new technologies to be applied in improving vehicle safety, exploiting intelligent road infrastructure and enabling robotic vehicles operating in human environments.Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences 10/2010; 368(1928):4649-72. · 2.89 Impact Factor
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ABSTRACT: This paper presents an overview of a human-robotic system under development at Cornell which is capable of mapping an unknown environment, as well as discovering, tracking, and neutralizing several static and dynamic objects of interest. In addition, the robots can coordinate their individual tasks with one another without overly burdening a human operator. The testbed utilizes the Segway RMP platform, with lidar, vision, IMU and GPS sensors. The software draws from autonomous systems research, specifically in the areas of pose estimation target detection and tracking, motion and behavioral planning, and human robot interaction. This paper also details experimental scenarios of mapping, tracking, and neutralization presented by way of pictures, data, and movies.Proc SPIE 01/2011;
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ABSTRACT: A novel-tracking algorithm is presented as a computationally feasible, real-time solution to the joint estimation problem of data assignment and dynamic obstacle tracking from a potentially moving robotic platform. The algorithm implements a Rao-Blackwellized particle filter (RBPF) to factorize the joint estimation problem into 1) a data assignment problem solved via particle filter and 2) a multiple dynamic obstacle-tracking problem solved with efficient parametric filters. The parametric filters make use of a new target representation and stable features developed specifically for tracking full-size vehicles in a dense traffic environment. The algorithm is validated in real time, both in controlled experiments with full-size robotic vehicles and on data collected at the 2007 Defense Advanced Research Projects Agency (DARPA) Urban Challenge.IEEE Transactions on Robotics 03/2011; · 2.57 Impact Factor