Temporal camera relocalization estimates the pose with respect to each video
frame in sequence, as opposed to one-shot relocalization which focuses on a
still image. Even though the time dependency has been taken into account,
current temporal relocalization methods still generally underperform the
state-of-the-art one-shot approaches in terms of accuracy. In this work, we
improve the temporal relocalization method by using a network architecture that
incorporates Kalman filtering (KFNet) for online camera relocalization. In
particular, KFNet extends the scene coordinate regression problem to the time
domain in order to recursively establish 2D and 3D correspondences for the pose
determination. The network architecture design and the loss formulation are
based on Kalman filtering in the context of Bayesian learning. Extensive
experiments on multiple relocalization benchmarks demonstrate the high accuracy
of KFNet at the top of both one-shot and temporal relocalization approaches.
Our codes are released at this https URL

通过使用 Kalman 滤波网络改进了相机时空重定位方法，实现了极高的定位精度。

KFNet: 使用卡尔曼滤波学习时序相机定位

KFNet: Learning Temporal Camera Relocalization using Kalman Filtering

On-board estimation of the pose of an uncooperative target spacecraft is an
essential task for future on-orbit servicing and close-proximity formation
flying missions. However, two issues hinder reliable on-board monocular vision
based pose estimation: robustness to illumination conditions due to a lack of
reliable visual features and scarcity of image datasets required for training
and benchmarking. To address these two issues, this work details the design and
validation of a monocular vision based pose determination architecture for
spaceborne applications. The primary contribution to the state-of-the-art of
this work is the introduction of a novel pose determination method based on
Convolutional Neural Networks (CNN) to provide an initial guess of the pose in
real-time on-board. The method involves discretizing the pose space and
training the CNN with images corresponding to the resulting pose labels. Since
reliable training of the CNN requires massive image datasets and computational
resources, the parameters of the CNN must be determined prior to the mission
with synthetic imagery. Moreover, reliable training of the CNN requires
datasets that appropriately account for noise, color, and illumination
characteristics expected in orbit. Therefore, the secondary contribution of
this work is the introduction of an image synthesis pipeline, which is tailored
to generate high fidelity images of any spacecraft 3D model. The proposed
technique is scalable to spacecraft of different structural and physical
properties as well as robust to the dynamic illumination conditions of space.
Through metrics measuring classification and pose accuracy, it is shown that
the presented architecture has desirable robustness and scalable properties.

本文介绍了一种基于卷积神经网络的单目视觉姿态确定方法，涉及对姿态空间进行离散处理并使用 CNN 进行训练的设计和验证，同时提出了一种用于生成任何航天器 3D 模型高保真度图像的图像合成流水线，并通过分类和姿态准确性评估指标表明该体系结构具有理想的稳健性和可伸缩性。