A stochastic map for uncertain spatial relationships
Proceedings of the 4th international symposium on Robotics Research
Comparative study of Hough transform methods for circle finding
Image and Vision Computing - Special issue: 5th Alvey vision meeting
Neural Nets Trained by Genetic Algorithms for Collision Avoidance
Applied Intelligence
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
Real-Time Simultaneous Localisation and Mapping with a Single Camera
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Fast and accurate SLAM with Rao-Blackwellized particle filters
Robotics and Autonomous Systems
Clustering Using a Similarity Measure Based on Shared Near Neighbors
IEEE Transactions on Computers
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
SLAM in Indoor Environments using Omni-directional Vertical and Horizontal Line Features
Journal of Intelligent and Robotic Systems
Journal of Intelligent and Robotic Systems
DP-SLAM: fast, robust simultaneous localization and mapping without predetermined landmarks
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Rao-blackwellised particle filtering for dynamic Bayesian networks
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Fuzzy Systems
Adaptive thresholding by variational method
IEEE Transactions on Image Processing
Communication constraints multi-agent territory exploration task
Applied Intelligence
Reasoning about shadows in a mobile robot environment
Applied Intelligence
Block-matching algorithm based on harmony search optimization for motion estimation
Applied Intelligence
Strategies for avoiding preference profiling in agent-based e-commerce environments
Applied Intelligence
Incremental 3D reconstruction using Bayesian learning
Applied Intelligence
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This paper presents Neuro-Evolutionary Optimization SLAM (NeoSLAM) a novel approach to SLAM that uses a neural network (NN) to autonomously learn both a nonlinear motion model and the noise statistics of measurement data. The NN is trained using evolutionary optimization to learn the residual error of the motion model, which is then added to the odometry data to obtain the full motion model estimate. Stochastic optimization is used, to accommodate any kind of cost function. Prediction and correction are performed simultaneously within our neural framework, which implicitly integrates the motion and sensor models. An evolutionary programming (EP) algorithm is used to progressively refine the neural model until it generates a trajectory that is most consistent with the actual sensor measurements. During this learning process, NeoSLAM does not require any prior knowledge of motion or sensor models and shows consistently good performance regardless of the robot and the sensor noise type. Furthermore, NeoSLAM does not require the data association step at loop closing which is crucial in most other SLAM algorithms, but can still generate an accurate map. Experiments in various complex environments with widely-varying types of noise show that the learning capability of NeoSLAM ensures performance that is consistently less sensitive to noise and more accurate than that of other SLAM methods.