Journal Article

FZJ-2024-04411

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Visual odometry with neuromorphic resonator networks

Renner, A. (Corresponding author)FZJ* ; Supic, L. (Corresponding author) ; Danielescu, A. ; Indiveri, G. ; Frady, E. P. ; Sommer, F. T. (Corresponding author) ; Sandamirskaya, Y. (Corresponding author)

2024
Springer Nature Publishing London

This record in other databases:    

Please use a persistent id in citations: doi:10.1038/s42256-024-00846-2  doi:10.34734/FZJ-2024-04411

Abstract: Visual odometry (VO) is a method used to estimate self-motion of a mobile robot using visual sensors. Unlike odometry based on integrating differential measurements that can accumulate errors, such as inertial sensors or wheel encoders, VO is not compromised by drift. However, image-based VO is computationally demanding, limiting its application in use cases with low-latency, low-memory and low-energy requirements. Neuromorphic hardware offers low-power solutions to many vision and artificial intelligence problems, but designing such solutions is complicated and often has to be assembled from scratch. Here we propose the use of vector symbolic architecture (VSA) as an abstraction layer to design algorithms compatible with neuromorphic hardware. Building from a VSA model for scene analysis, described in our companion paper, we present a modular neuromorphic algorithm that achieves state-of-the-art performance on two-dimensional VO tasks. Specifically, the proposed algorithm stores and updates a working memory of the presented visual environment. Based on this working memory, a resonator network estimates the changing location and orientation of the camera. We experimentally validate the neuromorphic VSA-based approach to VO with two benchmarks: one based on an event-camera dataset and the other in a dynamic scene with a robotic task.

---

Contributing Institute(s):

1. Neuromorphic Software Eco System (PGI-15)

Research Program(s):

1. 5234 - Emerging NC Architectures (POF4-523) (POF4-523)

Appears in the scientific report 2024

---

Database coverage:
; ; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; DEAL Nature ; Essential Science Indicators ; IF >= 20 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

---