Rough computational methods on reducing cost of computation in Markov localization for mobile robots

Building ``always-on'' robots to be deployed over extended periods of time in real human environments is challenging for several reasons. Some fundamental questions that arise in the process include: 1) How can the robot reconcile unexpected differences between its observations and its outdated map of the world? 2) How can we scalably test robots for long-term autonomy? 3) Can a robot learn to predict its own failures, and their corresponding causes? 4) When the robot fails and is unable to recover autonomously, can it utilize partially specified, approximate human corrections to overcome its failures? We summarize our research towards addressing all of these questions. We present 1) Episodic non-Markov Localization to maintain the belief of the robot's location while explicitly reasoning about unmapped observations; 2) a 1,000km challenge to test for long-term autonomy; 3) feature-based and learning-based approaches to predicting failures; and 4) human-in-the-loop SLAM to overcome robot mapping errors, and SMT-based robot transition repair to overcome state machine failures.

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Markov Localization for Mobile Robots in Dynamic Environments

Journal of Artificial Intelligence Research ◽

10.1613/jair.616 ◽

1999 ◽

Vol 11 ◽

pp. 391-427 ◽

Cited By ~ 505

Author(s):

D. Fox ◽

W. Burgard ◽

S. Thrun

Keyword(s):

Mobile Robots ◽

Mobile Robotics ◽

Fundamental Problem ◽

Dynamic Environments ◽

Sensor Data ◽

Fine Grained ◽

Tour Guides ◽

Accurate Position ◽

Museum Tour ◽

Markov Localization

Localization, that is the estimation of a robot's location from sensor data, is a fundamental problem in mobile robotics. This papers presents a version of Markov localization which provides accurate position estimates and which is tailored towards dynamic environments. The key idea of Markov localization is to maintain a probability density over the space of all locations of a robot in its environment. Our approach represents this space metrically, using a fine-grained grid to approximate densities. It is able to globally localize the robot from scratch and to recover from localization failures. It is robust to approximate models of the environment (such as occupancy grid maps) and noisy sensors (such as ultrasound sensors). Our approach also includes a filtering technique which allows a mobile robot to reliably estimate its position even in densely populated environments in which crowds of people block the robot's sensors for extended periods of time. The method described here has been implemented and tested in several real-world applications of mobile robots, including the deployments of two mobile robots as interactive museum tour-guides.

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