Mapping a Suburb With a Single Camera Using a Biologically Inspired SLAM System

By Michael Milford, Gordon Fraser Wyeth. IEEE Transactions on robotics, 2008

391 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Cell Biology, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/0d954bcafe671eb42e67576c62185f411c530455

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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].”
Visual Place Recognition: A Survey (2016, methodology): “This problem can be somewhat ameliorated by the use of circular shifts as in [116] or by combining a bag-of-words approach with a Gist descriptor on segments of the image [17], [110].”
Visual simultaneous localization and mapping: a survey (2012, methodology): “The experiments carried out in (Milford and Wyeth 2008; Glover et al. 2010) shows a good performance in real-time tasks in both indoor and outdoor environments.”
Persistent Navigation and Mapping using a Biologically Inspired SLAM System (2010, background): “The simultaneous localization and mapping (SLAM) problem is often identified as one of the key challenges in mobile robotics that must be solved before robots can become part of everyday life in domestic homes and the workplace.”
High-fidelity simulation for evaluating robotic vision performance (2016, background): “There are some place recognition methods which have proven to work well with both condition and viewpoint variations as in [11], [13] and [17].”
Robust visual SLAM across seasons (2015, background): “In the related work [19], the authors crop an image into three parts and use one of these cropped regions for image matching.”
Appearance-Based Loop Closure Detection for Online Large-Scale and Long-Term Operation (2013, background): “Simultaneous localization and mapping (SLAM) [1] is the capability required by robots to build and update a map of their operating environment and to localize themselves in it.”
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).”
Appearance-only SLAM at large scale with FAB-MAP 2.0 (2011, background): “In the largest appearance-based navigation experiment we are aware of (Milford and Wyeth 2008), a set of biologically inspired approaches is employed.”
A Bayesian approach for place recognition (2012, methodology): “Milford and Wyeth demonstrate multiple loop closures over a large scale topologicalmap using a biologically inspired visual system [14] but require extensive parameter tuning.”
Highly scalable appearance-only SLAM - FAB-MAP 2.0 (2009, background): “For example, in the largest appearance-based navigation experiment we are aware of [3], a set of biologically inspired approaches is employed.”