Information-based objective functions for active data selection
Neural Computation
Autonomous Exploration: Driven by Uncertainty
IEEE Transactions on Pattern Analysis and Machine Intelligence
Insect-inspired robotic homing
Adaptive Behavior
The spatial semantic hierarchy
Artificial Intelligence
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Handbook of Computer Vision and Applications with Cdrom
Handbook of Computer Vision and Applications with Cdrom
A novel visual landmark matching for a biologically inspired homing
Pattern Recognition Letters
Globally Consistent Range Scan Alignment for Environment Mapping
Autonomous Robots
Learning View Graphs for Robot Navigation
Autonomous Robots - Special issue on autonomous agents
Fast, On-Line Learning of Globally Consistent Maps
Autonomous Robots
Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans
Journal of Intelligent and Robotic Systems
Vision-Based Homing with a Panoramic Stereo Sensor
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
A multilevel relaxation algorithm for simultaneous localization and mapping
IEEE Transactions on Robotics
Vision-based global localization and mapping for mobile robots
IEEE Transactions on Robotics
Evolution of homing navigation in a real mobile robot
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Embodied spatial cognition: Biological and artificial systems
Image and Vision Computing
Three 2D-warping schemes for visual robot navigation
Autonomous Robots
Active vision in robotic systems: A survey of recent developments
International Journal of Robotics Research
Robotics and Autonomous Systems
Cleaning robot navigation using panoramic views and particle clouds as landmarks
Robotics and Autonomous Systems
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Most recent robotic systems, capable of exploring unknown environments, use topological structures (graphs) as a spatial representation. Localization can be done by deriving an estimate of the global pose from landmark information. In this case navigation is tightly coupled to metric knowledge, and hence the derived control method is mainly pose-based. Alternative to continuous metric localization, it is also possible to base localization methods on weaker constraints, e.g. the similarity between images capturing the appearance of places or landmarks. In this case navigation can be controlled by a homing algorithm. Similarity based localization can be scaled to continuous metric localization by adding additional constraints, such as alignment of depth estimates.We present a method to scale a similarity based navigation system (the view-graph-model) to continuous metric localization. Instead of changing the landmark model, we embed the graph into the three dimensional pose space. Therefore, recalibration of the path integrator is only possible at discrete locations in the environment. The navigation behavior of the robot is controlled by a homing algorithm which combines three local navigation capabilities, obstacle avoidance, path integration, and scene based homing. This homing scheme allows automated adaptation to the environment. It is further used to compensate for path integration errors, and therefore allows to derive globally consistent pose estimates based on "weak" metric knowledge, i.e. knowledge solely derived from odometry. The system performance is tested with a robotic setup and with a simulated agent which explores a large, open, and cluttered environment.