Arunkumar Rathinam is currently works as a Research Associate at SnT, Interdisciplinary Center for Security, Reliability and Trust, University of Luxembourg. He is a member of the Computer Vision, Imaging and Machine Intelligence Research Group (CVI2) leading multiple Space projects and SPARK challenge activities . His research areas are vision-based spacecraft navigation, deep learning-based pose estimation and neuromorphic vision. He received his PhD degree from the UNSW, Sydney in 2019. His PhD was on developing SLAM approaches for an orbiting spacecraft around an asteroid to achieve autonomous mapping and navigation. Before joining SnT, he worked as a Research Associate at the Surrey Space Center, University of Surrey, UK.
PhD in Space Robotics, 2019
UNSW (Australia)
MSc in Space Science and Technology, 2015
University of Würzburg (Germany)
BE in Mechanical Engineering, 2007
Anna University (India)
We introduce a new dataset, SPADES - SPAcecraft Pose Estimation Dataset using Event Sensing, comprising simulated event data end real event data acquired in a controlled laboratory environment.
This survey describes the current Deep Learning (DL)-based methods for spacecraft pose estimation in a comprehensive manner. A comparison of algorithms is presented not only in terms of pose accuracy but also with a focus on network architectures and models’ sizes keeping potential deployment in mind.