Conference Paper

Rao-Blackwellized Particle Filtering for Mapping Dynamic Environments

Sibley Sch. of Mech. & Aerosp. Eng., Cornell Univ., Ithaca, NY
DOI: 10.1109/ROBOT.2007.364071 Conference: Robotics and Automation, 2007 IEEE International Conference on
Source: IEEE Xplore


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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    • "Before beginning to address anticipating the actions of other vehicles, it is clear that more reliable perception methods are required. The current state of the art of tracking from a moving vehicle was in the DUC; Team Cornell developed a formal Bayesian estimator that could track car-like obstacles reliably for tens of seconds (Miller & Campbell 2007), to less than 1 m, with less than 0.2 m s −1 accuracy. But, on occasion, a cement barrier would be mistaken for a car, or a large bush would be initialized with a strange velocity because of occlusion reasoning. "
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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. DOI:10.1098/rsta.2010.0110 · 2.15 Impact Factor
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    • "In these works, only the observations reflected from static objects are selected and used for SLAM process, while the moving obstacles are tracked to avoid collision, independently. In [8] and [9], same problem is solved by proposing single unified framework that includes both data association and target tracking, and builds the map for stationary parts of the environment. "
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    ABSTRACT: In this paper, we present a robust RBPF-SLAM algorithm for mobile robots in non-static environments. We propose an approach for sampling particles from multiple ancestor sets, not from just one prior set. This sampling method increases the robustness of SLAM algorithm, because some particles can be updated by only observations consistent with the map, even if observation at certain time step is corrupted by environmental changes. Corrupted observations are filtered out from recursive Bayesian update process by the proposed sampling method. We also present an intermediate path estimation method to use abandoned sensor information reflected from relocated objects for map update. The map can represent the changed configuration of non-static environment by the stored sensor information and the estimated path. Results of simulations and experiments in non-static environments show the robustness of proposed RBPF-SLAM algorithm using sonar sensors.
    IEEE International Conference on Robotics and Automation, ICRA 2010, Anchorage, Alaska, USA, 3-7 May 2010; 01/2010
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    • "Many teams in the contest performed implicit vehicle detection using the object position in the lane and size to identify vehicles(Leonard et al., 2008; Miller et al., 2008; Stanford Racing Team, 2007). Moving objects detected with lidar or using radar Doppler velocity were also often assumed to be vehicles. "
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    ABSTRACT: Mid-way through the 2007 DARPA Urban Challenge, MIT’s autonomous Land Rover LR3 ‘Talos’ and Team Cornell’s autonomous Chevrolet Tahoe ‘Skynet’ collided in a low-speed accident, one of the first well-documented collisions between two full-size autonomous vehicles. This collaborative study between MIT and Cornell examines the root causes of the collision, which are identified in both teams’ system designs. Systems-level descriptions of both autonomous vehicles are given, and additional detail is provided on sub-systems and algorithms implicated in the collision. A brief summary of robot–robot interactions during the race is presented, followed by an in-depth analysis of both robots’ behaviors leading up to and during the Skynet–Talos collision. Data logs from the vehicles are used to show the gulf between autonomous and human-driven vehicle behavior at low speeds and close proximities. Contributing factors are shown to be: (1) difficulties in sensor data association leading to phantom obstacles and an inability to detect slow moving vehicles, (2) failure to anticipate vehicle intent, and (3) an over emphasis on lane constraints versus vehicle proximity in motion planning. Eye contact between human road users is a crucial communications channel for slow-moving close encounters between vehicles. Inter-vehicle communication may play a similar role for autonomous vehicles; however, there are availability and denial-of-service issues to be addressed.
    The DARPA Urban Challenge: Autonomous Vehicles in City Traffic, George Air Force Base, Victorville, California, USA; 01/2009
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