Modeling a dynamic environment using a Bayesian multiple hypothesis approach
Artificial Intelligence
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Monte Carlo Localization with Mixture Proposal Distribution
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Robust global localization using clustered particle filtering
Eighteenth national conference on Artificial intelligence
3D-Position Tracking and Control for All-Terrain Robots (Springer Tracts in Advanced Robotics)
3D-Position Tracking and Control for All-Terrain Robots (Springer Tracts in Advanced Robotics)
Bayesian Filtering for Location Estimation
IEEE Pervasive Computing
Self-adaptive Monte Carlo localization for mobile robots using range sensors
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Toward a Unified Bayesian Approach to Hybrid Metric--Topological SLAM
IEEE Transactions on Robotics
Guaranteed robust nonlinear estimation with application to robot localization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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In order to achieve the autonomy of mobile robots, effective localization is a necessary prerequisite. In this paper, we propose an improved Monte Carlo localization algorithm using self-adaptive samples (abbreviated as SAMCL). By employing a pre-caching technique to reduce the online computational burden, SAMCL is more efficient than the regular MCL. Further, we define the concept of similar energy region (SER), which is a set of poses (grid cells) having similar energy with the robot in the robot space. By distributing global samples in SER instead of distributing randomly in the map, SAMCL obtains a better performance in localization. Position tracking, global localization and the kidnapped robot problem are the three sub-problems of the localization problem. Most localization approaches focus on solving one of these sub-problems. However, SAMCL solves all the three sub-problems together, thanks to self-adaptive samples that can automatically separate themselves into a global sample set and a local sample set according to needs. The validity and the efficiency of the SAMCL algorithm are demonstrated by both simulations and experiments carried out with different intentions. Extensive experimental results and comparisons are also given in this paper.