Pose Estimation and Map Formation with Spiking Neural Networks: towards Neuromorphic SLAM

In this paper, we investigate 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. We perform spiking network simulations of the developed architecture, interfaced to a simulated robotic vehicle. We then port the neuronal map formation architecture on two connected neuromorphic devices, one of which features on-board plasticity, and demonstrate the feasibility of a neuromorphic realization of simultaneous localization and mapping (SLAM).

[1]  Gordon Wyeth,et al.  Spatial cognition for robots , 2009, IEEE Robotics & Automation Magazine.

[2]  Giacomo Indiveri,et al.  Artificial Cognitive Systems: From VLSI Networks of Spiking Neurons to Neuromorphic Cognition , 2009, Cognitive Computation.

[3]  Gert Cauwenberghs,et al.  Neuromorphic Silicon Neuron Circuits , 2011, Front. Neurosci.

[4]  Surya Ganguli,et al.  Cell types for our sense of location: where we are and where we are going , 2017, Nature Neuroscience.

[5]  Angelo Arleo,et al.  Spatial cognition and neuro-mimetic navigation: a model of hippocampal place cell activity , 2000, Biological Cybernetics.

[6]  Gordon Wyeth,et al.  RatSLAM: a hippocampal model for simultaneous localization and mapping , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[7]  Giacomo Indiveri,et al.  A Systematic Method for Configuring VLSI Networks of Spiking Neurons , 2011, Neural Computation.

[8]  Giacomo Indiveri,et al.  On-chip unsupervised learning in winner-take-all networks of spiking neurons , 2017, 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[9]  Giacomo Indiveri,et al.  A Scalable Multicore Architecture With Heterogeneous Memory Structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs) , 2017, IEEE Transactions on Biomedical Circuits and Systems.

[10]  Andrew S. Cassidy,et al.  A million spiking-neuron integrated circuit with a scalable communication network and interface , 2014, Science.

[11]  Hong Wang,et al.  Loihi: A Neuromorphic Manycore Processor with On-Chip Learning , 2018, IEEE Micro.

[12]  Jörg Conradt,et al.  Cooperative SLAM on small mobile robots , 2015, 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[13]  Jörg Conradt,et al.  Hebbian Plasticity Realigns Grid Cell Activity with External Sensory Cues in Continuous Attractor Models , 2016, Front. Comput. Neurosci..

[14]  Rodrigo Alvarez-Icaza,et al.  Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations , 2014, Proceedings of the IEEE.

[15]  Romain Brette,et al.  The Brian Simulator , 2009, Front. Neurosci..

[16]  Yulia Sandamirskaya,et al.  Learning to reach after learning to look: A study of autonomy in learning sensorimotor transformations , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[17]  W. Maass,et al.  State-dependent computations: spatiotemporal processing in cortical networks , 2009, Nature Reviews Neuroscience.

[18]  Alejandra Barrera,et al.  Biologically-inspired robot spatial cognition based on rat neurophysiological studies , 2008, Auton. Robots.

[19]  Gerald M Edelman,et al.  Characterizing functional hippocampal pathways in a brain-based device as it solves a spatial memory task. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[20]  Yulia Sandamirskaya,et al.  Learning Sensorimotor Transformations with Dynamic Neural Fields , 2013, ICANN.

[21]  Giacomo Indiveri,et al.  A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses , 2015, Front. Neurosci..

[22]  Daniel Cremers,et al.  Event-based 3D SLAM with a depth-augmented dynamic vision sensor , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[23]  O. Blanke,et al.  Spatial memories in insects , 2009, Current Biology.

[24]  Alejandro Linares-Barranco,et al.  An approach to motor control for spike-based neuromorphic robotics , 2014, 2014 IEEE Biomedical Circuits and Systems Conference (BioCAS) Proceedings.

[25]  Jörg Conradt,et al.  Cortically inspired sensor fusion network for mobile robot egomotion estimation , 2015, Robotics Auton. Syst..

[26]  Surya P. N. Singh,et al.  V-REP: A versatile and scalable robot simulation framework , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[27]  Helge J. Ritter,et al.  Using haptics to extract object shape from rotational manipulations , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[28]  Helge J. Ritter,et al.  Correcting pose estimates during tactile exploration of object shape: a neuro-robotic study , 2014, 4th International Conference on Development and Learning and on Epigenetic Robotics.

[29]  Jörg Conradt,et al.  Autonomous indoor exploration with an event-based visual SLAM system , 2013, 2013 European Conference on Mobile Robots.

[30]  Roland Siegwart,et al.  Introduction to Autonomous Mobile Robots, Second Edition , 2011, Intelligent robotics and autonomous agents.

[31]  G. Indiveri,et al.  Neuromorphic architectures for spiking deep neural networks , 2015, 2015 IEEE International Electron Devices Meeting (IEDM).

[32]  Philippe Gaussier,et al.  Neurobiologically Inspired Mobile Robot Navigation and Planning , 2007, Frontiers in neurorobotics.

[33]  Walter Senn,et al.  Learning Real-World Stimuli in a Neural Network with Spike-Driven Synaptic Dynamics , 2007, Neural Computation.

[34]  Scott Koziol,et al.  A Neuromorphic Approach to Path Planning Using a Reconfigurable Neuron Array IC , 2014, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[35]  Giacomo Indiveri,et al.  Synthesizing cognition in neuromorphic electronic systems , 2013, Proceedings of the National Academy of Sciences.

[36]  Yulia Sandamirskaya,et al.  A Neuromorphic Approach to Path Integration: A Head-Direction Spiking Neural Network with Vision-driven Reset , 2018, 2018 IEEE International Symposium on Circuits and Systems (ISCAS).

[37]  Yulia Sandamirskaya,et al.  Dynamic neural fields as a step toward cognitive neuromorphic architectures , 2014, Front. Neurosci..

[38]  Luis A. Plana,et al.  Event-based neural computing on an autonomous mobile platform , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[39]  Alex M. Andrew,et al.  Spiking Neuron Models: Single Neurons, Populations, Plasticity , 2003 .