Imperial College Innovations Limited

Imperial College of Science, Technology and Medicine

Stefan Leutenegger

Andrew Davison

Robert Lukierski

John Brendan Mccormac

Ankur Handa

Thomas Whelan

Renato Fernando Salas Moreno

Ben Glocker

Certain examples described herein relate to estimating dimensions of an enclosed space such as a room using a monocular multi-directional camera device. In examples, a movement of the camera device around a point in a plane of movement is performed, such as by a robotic device. Using the monocular multi-directional camera device, a sequence of images are obtained at a plurality of different angular positions during the movement. Pose data is determined from the sequence of images. The pose data is determined using a set of features detected within the sequence of images. Depth values are then estimated by evaluating a volumetric function of the sequence of images and the pose data. A three dimensional volume is defined around a reference position of the camera device, wherein the three-dimensional volume has a two-dimensional polygonal cross-section within the plane of movement. The three dimensional volume is then fitted to the depth values to determine dimensions for the polygonal cross-section. These dimensions then provide an estimate for the shape of the enclosed space.

Estimating dimensions for an enclosed space using a multi-directional camera

Certain examples described herein enable semantically-labelled representations of a three-dimensional (3D) space to be generated from video data. In described examples, a 3D representation is a surface element or 'surfel' representation, where the geometry of the space is modelled using a plurality of surfaces that are defined within a 3D co-ordinate system. Object-label probability values for spatial elements of frames of video data may be determined using a two-dimensional image classifier. Surface elements that correspond to the spatial elements are identified based on a projection of the surface element representation using an estimated pose for a frame. Object-label probability values for the surface elements are then updated based on the object-label probability values for corresponding spatial elements. This results in a semantically-labelled 3D surface element representation of objects present in the video data. This data enables computer vision and/or robotic applications to make better use of the 3D representation.

Detecting objects in video data

Examples described herein relate to mapping a space using a multi-directional camera. This mapping may be performed with a robotic device comprising a monocular multi-directional camera device and at least one movement actuator. The mapping may generate an occupancy map to determine navigable portions of the space. A robotic device movement around a point in a plane of movement may be instructed using the at least one movement actuator. Using the monocular multi-directional camera device, a sequence of images are obtained () at different angular positions during the instructed movement. Pose data is determined () from the sequence of images. The pose data is determined using features detected within the sequence of images. Depth values are then estimated () by evaluating a volumetric function of the sequence of images and the pose data. The depth values are processed () to populate the occupancy map for the space.

Mapping a space using a multi-directional camera

Certain examples described herein relate to modelling a three-dimensional space. Image data from at least one capture device is used to generate a three-dimensional model of the three-dimensional space. In certain examples, the three-dimensional model is segmented into active and inactive portions based on at least one model property. The examples are configured to use the active portions to update the three-dimensional model over time. Registration is also performed to align active portions of the three-dimensional model with inactive portions of the three-dimensional model over time. The registration aligns active portions of the three-dimensional model generated following an observation of a region of the three-dimensional space with inactive portions of the model generated following at least one previous observation of said region.

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Stefan Leutenegger