A Perception-Driven Autonomous Urban Vehicle.
In proceeding of: The DARPA Urban Challenge: Autonomous Vehicles in City Traffic, George Air Force Base, Victorville, California, USA
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Conference Paper: Positive and negative obstacle detection using the HLD classifier.[Show abstract] [Hide abstract]
ABSTRACT: Autonomous robots must be able to detect haz- ardous terrain even when sensor data is noisy and incomplete. In particular, negative obstacles such as cliffs or stairs often cannot be sensed directly; rather, their presence must be inferred. In this paper, we describe the height-length-density (HLD) terrain classifier that generalizes some prior methods and provides a unified mechanism for detecting both posi- tive and negative obstacles. The classifier utilizes three novel features that inherently deal with partial observability. The structure of the classifier allows the system designer to encode the capabilities of the vehicle as well as a notion of risk, making our approach applicable to virtually any vehicle. We evaluate our method in an indoor/outdoor environment, which includes several perceptually difficult real-world cases, and show that our approach out-performs current methods.2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2011, San Francisco, CA, USA, September 25-30, 2011; 01/2011
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ABSTRACT: We present two visual servoing controllers (pose-based and image-based) enabling mobile robots with a fixed pinhole camera to reach and follow a continuous path drawn on the ground. The first contribution is the theoretical and experimental comparison between pose-based and image-based techniques for a nonholonomic robot task. Moreover, our controllers are appropriate not only for path following, but also for path reaching, a problem that has been rarely tackled in the past. Finally, in contrast with most works, which require the path geometric model, only two path features are necessary in our image-based scheme and three in the pose-based scheme. For both controllers, a convergence analysis is carried out, and the performance is validated by simulations, and outdoor experiments on a car-like robot.Robotica 01/2011; 29:1037-1048. · 0.88 Impact Factor
Conference Paper: A passive solution to the sensor synchronization problem[Show abstract] [Hide abstract]
ABSTRACT: Knowing the time at which sensors acquired data is critical to the proper processing and interpretation of that data, particularly for mobile robots attempting to project sensor data into a consistent coordinate frame. Unfortunately, many popular commercial sensors provide no support for synchronization, rendering conventional synchronization algorithms useless. In this paper, we describe a passive synchronization algorithm that can significantly reduce timing error versus naively time-stamping sensor data when it arrives at the host. It is passive in the sense that the algorithm requires no special cooperation from the sensor. Our method estimates the timing jitter induced by hosts, and thus does not require a real-time operating system. We rigorously derive and characterize the method, proving that it can only improve upon the synchronization accuracy of the standard approach.Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on; 11/2010
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