TY - GEN
T1 - Real-Time robot tracking and following with neuromorphic vision sensor
AU - Mishra, Abhishek
AU - Ghosh, Rohan
AU - Goyal, Ashish
AU - Thakor, Nitish V.
AU - Kukreja, Sunil L.
N1 - Publisher Copyright:
© 2016 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2016/7/26
Y1 - 2016/7/26
N2 - In this paper, we consider the problem of robotic motion tracking and following with neuromorphic vision sensors. We formulate the problem in a leader-follower paradigm. The objective of the follower robot is to perform real-Time motion segmentation of a scene and follow the leader robot. Motion segmentation using a neuromorphic vision sensor mounted on a mobile robot is a challenging task due to events created by movements of the platform (self-movement). Current approaches for tracking do not perform well during sensor ego-motion or need a priori knowledge about the object being tracked. To address these limitations, we designed an algorithm based on clustering space-Time events induced by a neuromorphic sensor followed by a classification procedure. This technique is based on a distance transformation of existing sets. After clustering, a binary class label is assigned to each: (1) background or (2) moving object. The classifier uses event rates of clusters to determine proper class labels. The proposed technique forms an important module for the creation of collectively intelligent multi-pedal robots that utilize neuromorphic vision sensors. The utility and robustness of our algorithm is demonstrated as a real-Time online system implemented on two hexapod robots.
AB - In this paper, we consider the problem of robotic motion tracking and following with neuromorphic vision sensors. We formulate the problem in a leader-follower paradigm. The objective of the follower robot is to perform real-Time motion segmentation of a scene and follow the leader robot. Motion segmentation using a neuromorphic vision sensor mounted on a mobile robot is a challenging task due to events created by movements of the platform (self-movement). Current approaches for tracking do not perform well during sensor ego-motion or need a priori knowledge about the object being tracked. To address these limitations, we designed an algorithm based on clustering space-Time events induced by a neuromorphic sensor followed by a classification procedure. This technique is based on a distance transformation of existing sets. After clustering, a binary class label is assigned to each: (1) background or (2) moving object. The classifier uses event rates of clusters to determine proper class labels. The proposed technique forms an important module for the creation of collectively intelligent multi-pedal robots that utilize neuromorphic vision sensors. The utility and robustness of our algorithm is demonstrated as a real-Time online system implemented on two hexapod robots.
UR - http://www.scopus.com/inward/record.url?scp=84983401244&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84983401244&partnerID=8YFLogxK
U2 - 10.1109/BIOROB.2016.7523451
DO - 10.1109/BIOROB.2016.7523451
M3 - Conference contribution
AN - SCOPUS:84983401244
T3 - Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics
SP - 13
EP - 18
BT - 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics, BioRob 2016
PB - IEEE Computer Society
T2 - 6th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2016
Y2 - 26 June 2016 through 29 June 2016
ER -