Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots
Machine Learning - Special issue on learning in autonomous robots
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Building topological maps using minimalistic sensor models
Building topological maps using minimalistic sensor models
Towards a general theory of topological maps
Artificial Intelligence
Learning topological maps with weak local odometric information
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A Probabilistic Approach to Appearance-Based Localization and Mapping
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Robotics and Autonomous Systems
Cleaning robot navigation using panoramic views and particle clouds as landmarks
Robotics and Autonomous Systems
Anytime merging of appearance-based maps
Autonomous Robots
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This paper addresses the problem of localization and map construction by a mobile robot in an indoor environment. Instead of trying to build high-fidelity geometric maps, we focus on constructing topological maps as they are less sensitive to poor odometry estimates and position errors. We propose a modification to the standard SLAM algorithm in which the assumption that the robots can obtain metric distance/bearing information to landmarks is relaxed. Instead, the robot registers a distinctive sensor "signature", based on its current location, which is used to match robot positions. In our formulation of this non-linear estimation problem, we infer implicit position measurements from an image recognition algorithm. We propose a method for incrementally building topological maps for a robot which uses a panoramic camera to obtain images at various locations along its path and uses the features it tracks in the images to update the topological map. The method is very general and does not require the environment to have uniquely distinctive features. Two algorithms are implemented to address this problem. The Iterated form of the Extended Kalman Filter (IEKF) and a batch-processed linearized ML estimator are compared under various odometric noise models.