Stanley: The robot that won the DARPA Grand Challenge
supplied by Smart Microwave Sensors. Both RADAR sensors cover the frontal area up to 200 m, with a cov-erage angle in azimuth of about 20°. Two antennae of this system are mounted on both sides of the laser sensor array. The lasers, camera, and radar system Figure 4. a View of the vehicle’s roof rack with sensors.
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Robotic Mapping: A Survey - Sebastian Thrun
robots.stanford.eduThe problem of robotic mapping is that of acquiring a spatial model of a robot’s environment. Maps are commonly used for robot navigation (e.g., localization) [7, 51].
Probabilistic Algorithms in Robotics - Sebastian Thrun
robots.stanford.eduthe following: A robot that carries a notion of its own uncertainty and that acts accordingly, will do better than one that does not. In particular, probabilisticapproaches are typically more robust in …
Particle Filters in Robotics - Sebastian Thrun
robots.stanford.eduamples include the simultaneous localization and mapping problem [8, 27, 36, 45], which phrased as a state estima-tion problem involves a variable number of dimensions. A recent particle-filter algorithm known as FastSLAM [34] has been demonstrated to solve problems with more than
Simultaneous Localization and Mapping - Sebastian Thrun
robots.stanford.eduThe simultaneous localization and mapping (SLAM) problem has attracted immense attention in the mobile robotics literature [17], and SLAM techniques are at the core of many successful robot systems. Most researchers on SLAM assume that the unknown environment is static,
FastSLAM: A Factored Solution to the Simultaneous ...
robots.stanford.eduFastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem Michael Montemerlo and Sebastian Thrun School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 [email protected], [email protected] Daphne Koller and Ben Wegbreit Computer Science Department Stanford University Stanford, CA 94305-9010
Pyramidal Implementation of the Lucas Kanade Feature ...
robots.stanford.edu2 Description of the tracking algorithm The two key components to any feature tracker are accuracy and robustness. The accuracy component relates to ... is propagated to the next level L 1 by passing the new initial guess gL 1 of expression: gL 1 = 2(gL + dL): (7) The next level optical ow residual vector dL 1 is then computed through the same ...
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