Leveraging Neural Radiance Fields for Pose Estimation of an Unknown Space Object during Proximity Operations

• URL: http://arxiv.org/abs/2405.12728v3
• Date: Tue, 11 Jun 2024 09:42:29 GMT
• Title: Leveraging Neural Radiance Fields for Pose Estimation of an Unknown Space Object during Proximity Operations
• Authors: Antoine Legrand, Renaud Detry, Christophe De Vleeschouwer,
• Abstract summary: We present a novel method that enables an "off-the-shelf" spacecraft pose estimator to be applied on an unknown target. We train the NeRF model using a sparse collection of images that depict the target, and in turn generate a large dataset that is diverse both in terms of viewpoint and illumination. We demonstrate that our method successfully enables the training of an off-the-shelf spacecraft pose estimation network from a sparse set of images.
• Score: 14.624172952608653
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address the estimation of the 6D pose of an unknown target spacecraft relative to a monocular camera, a key step towards the autonomous rendezvous and proximity operations required by future Active Debris Removal missions. We present a novel method that enables an "off-the-shelf" spacecraft pose estimator, which is supposed to known the target CAD model, to be applied on an unknown target. Our method relies on an in-the wild NeRF, i.e., a Neural Radiance Field that employs learnable appearance embeddings to represent varying illumination conditions found in natural scenes. We train the NeRF model using a sparse collection of images that depict the target, and in turn generate a large dataset that is diverse both in terms of viewpoint and illumination. This dataset is then used to train the pose estimation network. We validate our method on the Hardware-In-the-Loop images of SPEED+ that emulate lighting conditions close to those encountered on orbit. We demonstrate that our method successfully enables the training of an off-the-shelf spacecraft pose estimation network from a sparse set of images. Furthermore, we show that a network trained using our method performs similarly to a model trained on synthetic images generated using the CAD model of the target.

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