Episodic non-Markov localization

2017 ◽  
Vol 87 ◽  
pp. 162-176 ◽  
Author(s):  
Joydeep Biswas ◽  
Manuela M. Veloso
Keyword(s):  
Markov Localization

Active Markov localization for mobile robots

1998 ◽  
Vol 25 (3-4) ◽  
pp. 195-207 ◽  
Author(s):  
Dieter Fox ◽  
Wolfram Burgard ◽  
Sebastian Thrun
Keyword(s):  
Mobile Robots ◽  
Markov Localization

scholarly journals The Quest For "Always-On" Autonomous Mobile Robots

2019 ◽  
Author(s):  
Joydeep Biswas
Keyword(s):  
Mobile Robots ◽  
State Machine ◽  
Autonomous Mobile Robots ◽  
Human In The Loop ◽  
The World ◽  
Feature Based ◽  
Markov Localization

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arise in the process include: 1) How can the robot reconcile unexpected differences between its observations and its outdated map of the world? 2) How can we scalably test robots for long-term autonomy? 3) Can a robot learn to predict its own failures, and their corresponding causes?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.

Probabilistic Approach to Mobile Robot Localization Based on Gaussian Models of Sensors

2014 ◽  
Vol 607 ◽  
pp. 803-810
Author(s):  
František Duchoň ◽  
Andrej Babinec ◽  
Jozef Rodina ◽  
Tomas Fico ◽  
Peter Hubinský
Keyword(s):  
Mobile Robot ◽  
Probabilistic Approach ◽  
Gaussian Model ◽  
Main Body ◽  
Robot Localization ◽  
Article Deal ◽  
Mobile Robot Localization ◽  
Real Robot ◽  
Probabilistic Localization ◽  
Markov Localization

In this paper the probabilistic approach to mobile robot localization is discussed. Generally probabilistic localization uses some type of sensors model. In this paper Gaussian model, which is the most appropriate probabilistic model of the sensors, is used. The main body of the article deal with the proposal of own approach to probabilistic localization, which is inspired by Markov localization. That is why the Markov localization is described in the introduction of the article. At the end of the article several experiments with the real robot are described. Results of the experiments have proven that proposed localization is accurate, fast and reliable.

Vision-based Markov localization for long-term autonomy

2017 ◽  
Vol 89 ◽  
pp. 147-157 ◽  
Author(s):  
Tayyab Naseer ◽  
Benjamin Suger ◽  
Michael Ruhnke ◽  
Wolfram Burgard
Keyword(s):  
Markov Localization
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