Imperial College Innovations Limited

Michael Bloesch

Andrew Davison

Stefan Leutenegger

Stephen Lloyd James

John Brendan Mccormac

Tristan William Laidlow

Ronald Clark

Charles Fletcher Houseago

Shuaifeng Zhi

Certain examples described herein relate to a system for processing image data. In such examples, the system includes an input interface to receive the image data, which is representative of at least one view of a scene. The system also includes an initialisation engine to generate a first latent representation associated with a first segmentation of at least a first view of the scene, wherein the first segmentation is a semantic segmentation. The initialisation engine is also arranged to generate a second latent representation associated with at least a second view of the scene. The system additionally includes an optimisation engine to jointly optimise the first latent representation and the second latent representation, in a latent space, to obtain an optimised first latent representation and an optimised second latent representation.

Scene representation using image processing

An image processing system configured to obtain a mesh representation of a scene, wherein the mesh representation comprises a plurality of polygons defined by respective vertices associated with an in-plane position, the in-plane position being in a plane comprising a first dimension and a second dimension, and the vertices having an associated vertex depth value in a third dimension different from the first dimension and the second dimension. The image processing system comprises an in-plane position estimation network configured to process image data representative of an image of the scene to estimate the in-plane positions associated with respective vertices of the mesh representation. The image processing system further comprises a depth estimation engine configured to process the in-plane positions and the image data to estimate the associated vertex depth values for the respective vertices of the mesh representation.

A method comprising applying an object recognition pipeline to frames of video data. The object recognition pipeline provides a mask output of objects detected in the frames. The method includes fusing the mask output of the object recognition pipeline with depth data associated with the frames of video data to generate a map of object instances, including projecting the mask output to a model space for the map of object instances using a camera pose estimate and the depth data. An object instance in the map of object instances is defined using surface-distance metric values within a three-dimensional object volume, and has an object pose estimate indicating a transformation of the object instance to the model space. The object pose estimate and the camera pose estimate form nodes of a pose graph for the map of model instances.

Mapping object instances using video data

A control system for a robotic device comprising a task embedding network to receive one or more demonstrations of a task and to generate a task embedding. The task embedding comprises a representation of the task, and each demonstration comprises one or more observations of a performance of the task. The control system includes a control network to receive the task embedding from the task embedding network and to apply a policy to map a plurality of successive observations of the robotic device to respective control instructions for the robotic device. The policy applied by the control network is modulated across the plurality of successive observations of the robotic device using the task embedding from the task embedding network.

Device control using policy training based on task embeddings

Certain examples described herein enable a robotic device to accurate map a surrounding environment. The robotic device uses an image capture device and at least one of the image capture device and the robotic device move within the environment. Measurements associated with movement of at least one of the image capture device and the robotic device are used to determine a state of the robotic device. The state of the robotic device models the image capture device and the robotic device with respect to a model of the environment that is constructed by a mapping engine. By comparing the state of the robotic device with a measured change in the robotic device, an accurate representation of the state of the robotic device may be constructed. This state is used by the mapping engine to update the model of the environment.

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London, United Kingdom

Michael Bloesch