Map learning with uninterpreted sensors and effectors
Artificial Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots
Machine Learning - Special issue on learning in autonomous robots
Experimental Comparison of Techniques for Localization and Mapping Using a Bearing-Only Sensor
ISER '00 Experimental Robotics VII
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
Exploring artificial intelligence in the new millennium
ICML '05 Proceedings of the 22nd international conference on Machine learning
WiFi-SLAM using Gaussian process latent variable models
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Qualitative map learning based on co-visibility of objects
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Map Building by Sequential Estimation of Inter-feature Distances
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
An incremental learning algorithm for optimizing high-dimensional ANN-based classification systems
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Map building without localization by estimation of inter-feature distances
Intelligent Data Analysis - Artificial Intelligence
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This paper proposes a new map building framework for mobile robot named Localization-Free Mapping by Dimensionality Reduction (LFMDR). In this framework, the robot map building is interpreted as a problem of reconstructing the 2-D coordinates of objects so that they maximally preserve the local proximity of the objects in the space of robot's observation history. Not only traditional linear PCA but also recent manifold learning techniques can be used for solving this problem. In contrast to the SLAM framework, LFMDR framework does not require localization procedures nor explicit measurement and motion models. In the latter part of this paper, we will demonstrate "visibility-only" and "bearing-only" localization-free mappings which are derived by applying LFMDR framework to the visibility and bearing measurements respectively.