Biologically inspired visual odometry based on the computational model of grid cells

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, …

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How to unlock the secrets of 3D navigation in the brain?

A Nature  News Feature report the research story of 3D navigation in  Nachum Ulanovsky lab. Titled “100 bats and a long, dark tunnel: one neuroscientist’s quest to unlock the secrets of 3D navigation” published in Nature News at …

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Path Integration in a Continuous Attractor Network Model

The excerpt note is about path integration with continuous attractor network according to McNaughton B. L., et al., 2006.

McNaughton, Bruce L., Francesco P. Battaglia, Ole Jensen, Edvard I. Moser, and May-Britt Moser. “Path integration and the neural basis

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Why is it a so difficult problem for navigating in a three-dimensional world?

The excerpt note is about spatial cognition in non-horizontal environments by Jeffery K. J. et al., 2013.

Jeffery, Kathryn J., Aleksandar Jovalekic, Madeleine Verriotis, and Robin Hayman. “Navigating in a three-dimensional world.” Behavioral and Brain Sciences 36, no. …

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Learning to Navigate

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, …

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How to build spatial representation model (x, y, z, yaw) for 3D SLAM inspired by the brain?

The excerpt note is some relevant references about 2.5D SLAM inspired by the brain, which is expanded from the RatSLAM system.

Guth F. A. et. al. present an Hippo 3D (DolphinSLAM), expanded from the RatSLAM system, which was initially designed …

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3D SLAM: the grand challenge and ultimate goal for robotic perception.

Real-world applications in GPS-denied environments require robust mapping and perception techniques to enable mobile systems to autonomously navigate complex 3D environments.

Robotic environments are in general 3D, involving translation in three directions, x, y, and z, and rotation around three …

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