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作者机构:The Fraunhofer IIS Fraunhofer Institute for Integrated Circuits IIS Nuremberg Germany The Statistical Learning and Data Science LMU Munich Munich Germany Munich Germany The Technical University of Dortmund Germany The Institute of Neural Information Processing Ulm University Ulm Germany
出 版 物:《arXiv》 (arXiv)
年 卷 期:2022年
核心收录:
摘 要:Visual-inertial localization is a key problem in computer vision and robotics applications such as virtual reality, self-driving cars, and aerial vehicles. The goal is to estimate an accurate pose of an object when either the environment or the dynamics are known. Absolute pose regression (APR) techniques directly regress the absolute pose from an image input in a known scene using convolutional and spatio-temporal networks. Odometry methods perform relative pose regression (RPR) that predicts the relative pose from a known object dynamic (visual or inertial inputs). The localization task can be improved by retrieving information from both data sources for a cross-modal setup, which is a challenging problem due to contradictory tasks. In this work, we conduct a benchmark to evaluate deep multimodal fusion based on pose graph optimization and attention networks. Auxiliary and Bayesian learning are utilized for the APR task. We show accuracy improvements for the APR-RPR task and for the RPR-RPR task for aerial vehicles and hand-held devices. We conduct experiments on the EuRoC MAV and PennCOSYVIO datasets and record and evaluate a novel industry *** Codes 68T40, 65D19 Copyright © 2022, The Authors. All rights reserved.