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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deep Learning-Based Covariance Estimation for Relative Pose Measurements


    Contributors:


    Publication date :

    2024-09-24


    Size :

    673450 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    RCPNet: Deep-Learning based Relative Camera Pose Estimation for UAVs

    Yang, Chenhao / Liu, Yuyi / Zell, Andreas | IEEE | 2020


    FPGA Hardware Acceleration for Deep Learning-Based Satellite Relative Pose Estimation

    Capuano, Giovanni Maria / Capuano, Vincenzo / Napolano, Giuseppe et al. | AIAA | 2025


    Optimal Pose Estimation with Error-Covariance Analysis

    Cheng, Yang / Crassidis, John L. | AIAA | 2021


    OPTIMAL POSE ESTIMATION WITH ERROR-COVARIANCE ANALYSIS

    Cheng, Yang / Crassidis, John L. | TIBKAT | 2021


    Deep Monocular Relative 6D Pose Estimation for Ship-Based Autonomous UAV

    Wickramasuriya, Maneesha / Lee, Taeyoung / Snyder, Murray | AIAA | 2024