Ahrabian, Alireza (Hitachi Europe Limited), Nguyen, Quan (Hitachi Europe Limited), Toulios, Nikos (Hitachi ERD), Ohazulike, Anthony (Hitachi Europe SAS)

Deep Learning-Based Covariance Estimation for Relative Pose Measurements

Scheduled for presentation during the Invited Session "Self-Assessment of Perception Systems" (WeAT5), Wednesday, September 25, 2024, 10:50−11:10, Salon 13

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 14, 2024

Abstract

We propose a general covariance estimation method for relative pose measurements using deep learning. Our approach extends previous system specific covariance estimation models. Such models map input images acquired from two different viewpoints to a covariance estimate. While such models have successfully been applied to relative pose measurements obtained from visual odometry, the extension to the general system scenario is rather more challenging. In this paper, we propose to map both the inputs images acquired from two viewpoints along with the relative pose measurement to a covariance estimate. By including the relative pose measurement as an additional input to the mapping, we show that it is possible to predict covariance for general relative pose measurements.