Persistent Navigation and Mapping using a Biologically Inspired SLAM System

@article{Milford2010PersistentNA,
  title={Persistent Navigation and Mapping using a Biologically Inspired SLAM System},
  author={Michael Milford and Gordon Wyeth},
  journal={The International Journal of Robotics Research},
  year={2010},
  volume={29},
  pages={1131 - 1153}
}
The challenge of persistent navigation and mapping is to develop an autonomous robot system that can simultaneously localize, map and navigate over the lifetime of the robot with little or no human intervention. Most solutions to the simultaneous localization and mapping (SLAM) problem aim to produce highly accurate maps of areas that are assumed to be static. In contrast, solutions for persistent navigation and mapping must produce reliable goal-directed navigation outcomes in an environment… 
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References

SHOWING 1-10 OF 58 REFERENCES
FastSLAM: a factored solution to the simultaneous localization and mapping problem
TLDR
This paper presents FastSLAM, an algorithm that recursively estimates the full posterior distribution over robot pose and landmark locations, yet scales logarithmically with the number of landmarks in the map.
Mapping a Suburb With a Single Camera Using a Biologically Inspired SLAM System
TLDR
A biologically inspired approach to vision-only simultaneous localization and mapping (SLAM) on ground-based platforms based on computational models of the rodent hippocampus is described, coupled with a lightweight vision system that provides odometry and appearance information.
An adaptive appearance-based map for long-term topological localization of mobile robots
  • Feras DayoubT. Duckett
  • Computer Science
    2008 IEEE/RSJ International Conference on Intelligent Robots and Systems
  • 2008
TLDR
This paper presents a method for creating an adaptive map for long-term appearance-based localization of a mobile robot using long- term and short-term memory concepts, with omni-directional vision as the external sensor.
Robotic and neuronal simulation of the hippocampus and rat navigation.
TLDR
A neuronal simulation of the firing of place cells in open-field environments of varying shape is presented, coupled with an existing model of how place-cell firing can be used to drive navigation, and is tested by implementation as a miniature mobile robot.
Autonomous vision-based navigation: Goal-oriented action planning by transient states prediction, cognitive map building, and sensory-motor learning
TLDR
A bio-inspired neural network providing planning capabilities in autonomous navigation applications that autonomously learns a stable representation of its environment during a long random walk and proves to be able to return to the goal from any position of the environment.
Outdoor Simultaneous Localisation and Mapping Using RatSLAM
TLDR
The method, RatSLAM, is based upon computational models of the area in the rat brain that maintains the rodent’s idea of its position in the world and uses the visual appearance of different locations to build hybrid spatial-topological maps of places it has experienced that facilitate relocalisation and path planning.
A Minimalistic Approach to Appearance-Based Visual SLAM
This paper presents a vision-based approach to simultaneous localization and mapping (SLAM) in indoor/outdoor environments with minimalistic sensing and computational requirements. The approach is
RatSLAM: a hippocampal model for simultaneous localization and mapping
TLDR
RatSLAM is an implementation of a hippocampal model that can perform SLAM in real time on a real robot, and uses a competitive attractor network to integrate odometric information with landmark sensing to form a consistent representation of the environment.
Experience mapping: Producing spatially continuous environment representations using RatSLAM
TLDR
A new techniqueknown as experience mapping is described that can be used on-line with the RatSLAM system to produce world representations known as experience maps, which group together multiple place representations and are spatially continuous.
A multilevel relaxation algorithm for simultaneous localization and mapping
TLDR
An incremental SLAM algorithm is introduced that is derived from multigrid methods used for solving partial differential equations, which has an update time that is linear in the number of estimated features for typical indoor environments, even when closing very large loops.
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