Persistent Navigation and Mapping using a Biologically Inspired SLAM System

By Michael Milford, Gordon Fraser Wyeth. Int. J. Robotics Res., 2010

310 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/49c9a64c47b20426356e560f992e7afa9171dd34

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Citations in the corpus

SeqSLAM: Visual route-based navigation for sunny summer days and stormy winter nights (2012, methodology): “The approach taken in this work is partly inspired by the author's previous work in vision-based mapping in RatSLAM [16] [17] [18].”
Robust visual SLAM across seasons (2015, methodology): “There has been a tremendous progress in the area of visual Simultaneous Localization and Mapping (SLAM) over the last couple of years, especially in the context of appearancebased place recognition, which is an essential building block to successfully find loop closures in the context of SLAM [6], [”
DREAM Architecture: a Developmental Approach to Open-Ended Learning in Robotics (2020, unknown): “Many models of the hippocampus have been implemented on real robots [143, 144, 145, 146, 147, for example] or have inspired robot navigation algorithms [148, 31, for example], either to test the efﬁciency of neural theories in realistic settings, or as bioinspired tools for robotic navigation.”
Appearance-Based Loop Closure Detection for Online Large-Scale and Long-Term Operation (2013, methodology): “To identify the locations to keep in WM, the solution studied in this paper consists of keeping the most recent and frequently observed locations in WM, and transferring the others into Long-Term Memory (LTM).”
Visual teach and repeat for long‐range rover autonomy (2010, methodology): “Throughout this work, we use a generic localization module based on the stereo VO algorithm pioneered by Moravec (1980) and refined by Matthies (1989) and others.”
Visual Place Recognition: A Survey (2016, background): “During a two-week office-based experiment [225], Milford and Wyeth noted that " the weakness is that the system deals rather inefficiently with cyclic changes such as day-night time cycles.”
Practice makes perfect? Managing and leveraging visual experiences for lifelong navigation (2012, methodology): “We denote experiences by E and refer to the jth experience as jE .”
High-fidelity simulation for evaluating robotic vision performance (2016, methodology): “While this is not a state-of-the-art technique in and of itself, it has formed the foundation of major place recognition algorithms including RatSLAM [12] and SeqSLAM.”
Semi-parametric Topological Memory for Navigation (2018, methodology): “Milford and colleagues designed SLAM systems inspired by computational models of the hippocampus (Milford & Wyeth, 2008; 2010; Ball et al., 2013).”