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Dubbelman G.,TU Eindhoven | Browning B.,CMUs Robotics Institute
Proceedings - IEEE International Conference on Robotics and Automation | Year: 2013

A novel closed-form solution for pose-graph SLAM is presented. It optimizes pose-graphs of particular structure called pose-chains by employing an extended version of trajectory bending. Our solution is designed as a back-end optimizer to be used within systems whose front-end performs state-of-the-art visual odometry and appearance based loop detection. The optimality conditions of our closed-form method and that of state-of-the-art iterative methods are discussed. The practical relevance of their theoretical differences is investigated by extensive experiments using simulated and real data. It is shown using 49 kilometers of challenging binocular data that the accuracy obtained by our closed-form solution is comparable to that of state-of-the-art iterative solutions while the time it needs to compute its solution is a factor 50 to 200 times lower. This makes our approach relevant to a broad range of applications and computational platforms. © 2013 IEEE.

Bagnell J.,CMUs Robotics Institute | Bradley D.,NREC | Silver D.,Carnegie Mellon University | Sofman B.,Carnegie Mellon University | And 2 more authors.
IEEE Robotics and Automation Magazine | Year: 2010

Autonomous navigation by a mobile robot through natural, unstructured terrain is one of the premier challenges in field robotics. Tremendous advances in autonomous navigation have been made recently in field robotics [1][3]. Machine learning has played an increasingly important role in these advances. The Defense Advanced Research Projects Agency (DARPA) UGCV-Perceptor Integration (UPI) program was conceived to take a fresh approach to all aspects of autonomous outdoor mobile robot design, from vehicle design to the design of perception and control systems with the goal of achieving a leap in performance to enable the next generation of robotic applications in commercial, industrial, and military applications. The essential problem addressed by the UPI program is to enable safe autonomous traverse of a robot from Point A to Point B in the least time possible given a series of waypoints in complex, unstructured terrain separated by 0.22 km. To accomplish this goal, machine learning techniques were heavily used to provide robust and adaptive performance, while simultaneously reducing the required development and deployment time. This article describes the autonomous system, Crusher, developed for the UPI program and the learning approaches that aided in its successful performance. © 2006 IEEE.

Dubbelman G.,CMUs Robotics Institute | Hansen P.,CMUs Robotics Institute | Browning B.,CMUs Robotics Institute | Dias M.B.,CMUs Robotics Institute
Proceedings - IEEE International Conference on Robotics and Automation | Year: 2012

In earlier work closed-form trajectory bending was shown to provide an efficient and accurate out-of-core solution for loop-closing exactly sparse trajectories. Here we extend it to fuse exactly sparse trajectories, obtained from relative pose estimates, with absolute orientation data. This allows us to close-the-loop using absolute orientation data only. The benefit is that our approach does not rely on the observations from which the trajectory was estimated nor on the probabilistic links between poses in the trajectory. It therefore is highly efficient. The proposed method is compared against regular fusion and an iterative trajectory bending solution using a 5 km long urban trajectory. Proofs concerning optimality of our method are provided. © 2012 IEEE.

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