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: Midway through the 2007 DARPA Urban Challenge, MIT's robot “Talos” and Team Cornell's robot “Skynet” collided in a low-speed accident. This accident was one of the first collisions between full-sized autonomous road vehicles. Fortunately, both vehicles went on to finish the race and the collision was thoroughly documented in the vehicle logs. This collaborative study between MIT and Cornell traces the confluence of events that preceded the collision and examines its root causes. A summary of robot–robot interactions during the race is presented. The logs from both vehicles are used to show the gulf between robot and human-driver behavior at close vehicle proximities. Contributing factors are shown to be (1) difficulties in sensor data association leading to an inability to detect slow-moving vehicles and phantom obstacles, (2) failure to anticipate vehicle intent, and (3) an overemphasis on lane constraints versus vehicle proximity in motion planning. Finally, we discuss approaches that could address these issues in future systems, such as intervehicle communication, vehicle detection, and prioritized motion planning. © 2008 Wiley Periodicals, Inc.Journal of Field Robotics 09/2008; 25(10):775 - 807. · 2.15 Impact Factor
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ABSTRACT: The article proposes a solution to map-based self-localization for an autonomous robot operating in cluttered and crowded environments. To detect features for localization, 2D laser range-finders traditionally scan a plane parallel to the floor. This work hypothesizes the existence of a “low frequency cross-section” of the 3D Workspace where cluttered and dynamic environments become “more regular” and “less dynamic”. The contribution of the article is twofold. First, an “unevenness index” U is introduced to quantitatively measure the complexity of the environment as it would be perceived if the laser range-finder were located at different heights from the floor. The article shows that, by choosing the laser scanning plane to statistically minimize U (in most cases, above the heads of people), it is possible to deal more efficiently with non-linearities in the measurement model, moving objects and occluded features. Second, it is demonstrated that, when adopting an extended Kalman filter for position tracking (a very common and widely used technique in real-world scenarios), the a posteriori covariance of the estimated robot pose converges faster, on average, when U is lower, which leads to better localization performance. Experimental results show hours of continuous robot operation in real-world, cluttered and crowded environments.Journal of Intelligent and Robotic Systems 11/2012; 68(2). · 0.83 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;