Learning metric-topological maps for indoor mobile robot navigation
Artificial Intelligence
Artificial intelligence and mobile robots: case studies of successful robot systems
Artificial intelligence and mobile robots: case studies of successful robot systems
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Using EM to Learn 3D Models of Indoor Environments with Mobile Robots
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Sonar-Based Mapping of Large-Scale Mobile Robot Environments using EM
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Markov Localization using Correlation
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Coordination for Multi-Robot Exploration and Mapping
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A frontier-based approach for autonomous exploration
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
Occupancy grids: a probabilistic framework for robot perception and navigation
Occupancy grids: a probabilistic framework for robot perception and navigation
On the interdependence of sensing and estimation complexity in sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
Occupancy grids building by sonar and mobile robot
Robotics and Autonomous Systems
A Novel Measure of Uncertainty for Mobile Robot SLAM with Rao-Blackwellized Particle Filters
International Journal of Robotics Research
Fuzzy logic-based real-time robot navigation in unknown environment with dead ends
Robotics and Autonomous Systems
Vectorization of Grid Maps by an Evolutionary Algorithm
RoboCup 2006: Robot Soccer World Cup X
An Occupancy Grids Building Method with Sonar Sensors Based on Improved Neural Network Model
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Constructing dependable certainty grids from unreliable sensor data
Robotics and Autonomous Systems
Robotic Mapping Using Measurement Likelihood Filtering
International Journal of Robotics Research
Feature based occupancy grid maps for sonar based safe-mapping
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Exploring unknown environments with mobile robots using coverage maps
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Effective maximum likelihood grid map withconflict evaluation filter using sonar sensors
IEEE Transactions on Robotics
Improved Techniques for the Rao-Blackwellized Particle Filters SLAM
ICIRA '09 Proceedings of the 2nd International Conference on Intelligent Robotics and Applications
SLAM Estimation in Dynamic Outdoor Environments: A Review
ICIRA '09 Proceedings of the 2nd International Conference on Intelligent Robotics and Applications
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Contextual occupancy maps using Gaussian processes
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Optimal camera placement for total coverage
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Maximum-likelihood sample-based maps for mobile robots
Robotics and Autonomous Systems
Drawing stereo disparity images into occupancy grids: measurement model and fast implementation
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Bio-inspired architecture for active sensorimotor localization
SC'10 Proceedings of the 7th international conference on Spatial cognition
Increasing sensor measurements to reduce detection complexity in large-scale detection applications
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
Optimal Filtering for Non-parametric Observation Models: Applications to Localization and SLAM
International Journal of Robotics Research
Fast 3D time of flight data segmentation using the U-V-histogram approach
ICIRA'10 Proceedings of the Third international conference on Intelligent robotics and applications - Volume Part I
Gaussian process occupancy maps*
International Journal of Robotics Research
Laser and Radar Based Robotic Perception
Foundations and Trends in Robotics
Robotics and Autonomous Systems
A generative model for online depth fusion
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Evolutionary computation for intelligent self-localization in multiple mobile robots based on SLAM
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part I
A Kalman filter based approach to probabilistic gas distribution mapping
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Low-complexity scalable distributed multicamera tracking of humans
ACM Transactions on Sensor Networks (TOSN)
A Method of Boundary Following by a Wheeled Mobile Robot Based on Sampled Range Information
Journal of Intelligent and Robotic Systems
International Journal of Robotics Research
| Hi-index | 0.00 |
This article describes a new algorithm for acquiring occupancy grid maps with mobile robots. Existing occupancy grid mapping algorithms decompose the high-dimensional mapping problem into a collection of one-dimensional problems, where the occupancy of each grid cell is estimated independently. This induces conflicts that may lead to inconsistent maps, even for noise-free sensors. This article shows how to solve the mapping problem in the original, high-dimensional space, thereby maintaining all dependencies between neighboring cells. As a result, maps generated by our approach are often more accurate than those generated using traditional techniques. Our approach relies on a statistical formulation of the mapping problem using forward models. It employs the expectation maximization algorithm for searching maps that maximize the likelihood of the sensor measurements.