Convolutional Cross-View Pose Estimation

• URL: http://arxiv.org/abs/2303.05915v3
• Date: Fri, 22 Dec 2023 09:00:03 GMT
• Title: Convolutional Cross-View Pose Estimation
• Authors: Zimin Xia, Olaf Booij, and Julian F. P. Kooij
• Abstract summary: We propose a novel end-to-end method for cross-view pose estimation. Our method is validated on the VIGOR and KITTI datasets. On the Oxford RobotCar dataset, our method can reliably estimate the ego-vehicle's pose over time.
• Score: 9.599356978682108
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel end-to-end method for cross-view pose estimation. Given a ground-level query image and an aerial image that covers the query's local neighborhood, the 3 Degrees-of-Freedom camera pose of the query is estimated by matching its image descriptor to descriptors of local regions within the aerial image. The orientation-aware descriptors are obtained by using a translationally equivariant convolutional ground image encoder and contrastive learning. The Localization Decoder produces a dense probability distribution in a coarse-to-fine manner with a novel Localization Matching Upsampling module. A smaller Orientation Decoder produces a vector field to condition the orientation estimate on the localization. Our method is validated on the VIGOR and KITTI datasets, where it surpasses the state-of-the-art baseline by 72% and 36% in median localization error for comparable orientation estimation accuracy. The predicted probability distribution can represent localization ambiguity, and enables rejecting possible erroneous predictions. Without re-training, the model can infer on ground images with different field of views and utilize orientation priors if available. On the Oxford RobotCar dataset, our method can reliably estimate the ego-vehicle's pose over time, achieving a median localization error under 1 meter and a median orientation error of around 1 degree at 14 FPS.

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