State estimation in high dimensional space is a big challenge problem. Commonly, we use Lie group to implement 6DoF state estimation in 3D space. However, how does the brain implement path integration in 3D space based on neural dynamics?
Low …
Finkelstein, Arseny, Nachum Ulanovsky, Misha Tsodyks, and Johnatan Aljadeff. “Optimal dynamic coding by mixed-dimensionality neurons in the head-direction system of bats.” Nature communications 9, no. 1 (2018): 3590.
Abstract:
Ethologically relevant stimuli are often multidimensional. In many brain …
Lea Steffen, Daniel Reichard, Jakob Weinland, Jacques Kaiser, Arne Roennau and Rüdiger Dillmann. Neuromorphic Stereo Vision: a Survey of Bio-inspired Sensors and Algorithms. Front. Neurorobot. | doi: 10.3389/fnbot.2019.00028
Anstract: Any visual sensor, whether artificial or biological, maps the 3D-world …
Klukas, Mirko, Marcus Lewis, and Ila Fiete. “Flexible representation and memory of higher-dimensional cognitive variables with grid cells.” bioRxiv (2019): 578641.
The following content is from Klukas 2019.
Grid cell representations are simultaneously flexible and powerful yet rigid …
The following are some key references about 3D state estimation, 3D motion, 3D pose graph optimization.
Solà, Joan, Jeremie Deray, and Dinesh Atchuthan. “A micro Lie theory for state estimation in robotics.” arXiv preprint arXiv:1812.01537 (2018).
In this …
NeuroSLAM: Neural Simultaneous Localization and Mapping Workshop
Simultaneous Localization and Mapping (or SLAM) refers to the problem of constructing a map of an unknown environment as it is actively being explored. SLAM has been treated extensively in mobile robotics, providing …
J. Dupeyroux et al. 2019 presents a navigation system inspired by desert ants’ navigation behavior, which requires precise and robust sensory modalities.
They tested several ant-inspired solutions to outdoor homing navigation problems on a legged robot using two optical sensors …
The DeepMind opens the code of grid cells (Banino et al 2018) via GitHub(https://github.com/deepmind/grid-cells) in Jan. 2019. This package provides an implementation of the supervised learning experiments in Vector-based navigation using grid-like representations in artificial agents, as published …
The latest research Kreiser et al. 2018, published IROS 2018, investigated the use of ultra low-power, mixed signal analog/digital neuromorphic hardware for implementation of biologically inspired neuronal path integration and map formation for a mobile robot.
For further info, please …
3D Simultaneous Localization and Mapping and Navigation Planning for Mobile Robots in Complex Environments. June 28, 2017. By Professor Sven Behnke, University of Bonn, Germany…
3D Drone Localization and Mapping. Oct.10 ,2018. By Prof. Ioannis Pitas, Aristotle University of Thessaloniki. Contributors: J.M.M. Montiel (University of Zaragoza), J. Ramiro Martinez-de Dios (University of Seville), E. Kakaletsis, N. Nikolaidis (Aristotle University of Thessaloniki)…
3D Motion Estimation. Oct.10 ,2018. By Prof. Ioannis Pitas, Aristotle University of Thessaloniki.…
By Chris Edwards
Communications of the ACM, August 2018, Vol. 61 No. 8, Pages 14-16. 10.1145/3231168
Mammalian research has underpinned the key models used in robot development. Analogs of neural networks found in the rat’s brain underpin the most widespread …
A biologically inspired visual odometry based on the computational model of grid cells, which is developed based on the the source code of the computational model of grid cells: http://clm.utexas.edu/fietelab/code.htm, and LIBVISO2: http://www.cvlibs.net/software/libviso/, by Huimin Lu, Junhao Xiao, …
The excerpt note is about the novel approach of learning to navigate proposed by DeepMind research team in past few period of time.
How did you learn to navigate the neighborhood of your childhood, to go to a friend’s house, …
Brain Inspired Navigation Blog
New discovery worth spreading on brain-inspired navigation in neurorobotics and neuroscience