Machine Learning
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Reinforcement Learning in Continuous Time and Space
Neural Computation
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Indoor Navigation for a Humanoid Robot Using a View Sequence
International Journal of Robotics Research
Which landmark is useful?: learning selection policies for navigation in unknown environments
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
A visual odometry framework robust to motion blur
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Efficient vision-based navigation
Autonomous Robots
| Hi-index | 0.00 |
Cameras are popular sensors for robot navigation tasks such as localization as they are inexpensive, lightweight, and provide rich data. However, fast movements of a mobile robot typically reduce the performance of vision-based localization systems due to motion blur. In this paper, we present a reinforcement learning approach to choose appropriate velocity profiles for vision-based navigation. The learned policy minimizes the time to reach the destination and implicitly takes the impact of motion blur on observations into account. To reduce the size of the resulting policies, which is desirable in the context of memory-constrained systems, we compress the learned policy via a clustering approach. Extensive simulated and real-world experiments demonstrate that our learned policy significantly outperforms any policy that uses a constant velocity. We furthermore show, that our policy is applicable to different environments. Additional experiments demonstrate that our compressed policies do not result in a performance loss compared to the originally learned policy.