Efficient off-road localization using visually corrected odometry
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
A dynamically configurable coprocessor for convolutional neural networks
Proceedings of the 37th annual international symposium on Computer architecture
Finding and transferring policies using stored behaviors
Autonomous Robots
| Hi-index | 0.00 |
We present a multilayered mapping, planning, and command execution system developed and tested on the LAGR mobile robot. Key to robust performance under uncertainty is the combination of a short-range perception system operating at high frame rate and low resolution and a long-range, adaptive vision system operating at lower frame rate and higher resolution. The short-range module performs local planning and obstacle avoidance with fast reaction times, whereas the long-range module performs strategic visual planning. Probabilistic traversability labels provided by the perception modules are combined and accumulated into a robot-centered hyperbolic-polar map with a 200-m effective range. Instead of using a dynamical model of the robot for short-range planning, the system uses a large lookup table of physically possible trajectory segments recorded on the robot in a wide variety of driving conditions. Localization is performed using a combination of global positioning system, wheel odometry, inertial measurement unit, and a high-speed, low-complexity rotational visual odometry module. The end-to-end system was developed and tested on the LAGR mobile robot and was verified in independent government tests. © 2008 Wiley Periodicals, Inc.