Abstract: A method of localising a vehicle hosting a sensor comprises capturing data from the sensor providing a sensed scene around the vehicle at a current time, processing the sensed scene to extract a set of features from the sensed scene and determine a position of the vehicle from the sensed scene in relation to a previous position of the sensor, comparing the extracted set of features to one or more stored experiences, wherein each stored experience comprises a plurality of sets of features, where each set of features has been determined from a previously sensed scene, to ascertain whether the sensed scene can be recognised within any of the stored experiences, and if the extracted set of features of the sensed scene are recognised then that stored experience is used to produce an estimate of the position of the vehicle at the current time with respect to the stored experience.

Abstract: A method and apparatus are disclosed for localizing a vehicle along a route. The method comprises initially surveying the route to be traversed by the vehicle by creating a plurality of orthographic images of the route, at a plurality of locations along the route and subsequently passing the vehicle along the route while obtaining a current view of the route. The method further comprises resolving a pose of the vehicle relative to a survey trajectory by maximising a relation between the orthographic images and the current view.

Abstract: A method of determining extrinsic calibration parameters for at least one sensor (102, 104, 106) mounted on transportable apparatus (100). The method includes receiving (202) data representing pose history of the transportable apparatus and receiving (202) sensor data from at least one sensor mounted on transportable apparatus. The method generates (204) at least one point cloud data using the sensor data received from the at least one sensor, each point in a said point cloud having a point covariance derived from the pose history data. The method then maximises (206) a value of a quality function for the at least one point cloud, and uses (208) the maximised quality function to determine extrinsic calibration parameters for the at least one sensor.

Abstract: Method and apparatus for localising transportable apparatus (100) within an environment (120) includes obtaining (202) point cloud data (114) representing a 3D point cloud with appearance information of at least part of the environment. The method further obtains (204) first frame data (110) representing an image produced by a sensor (102) onboard transportable apparatus at a first time and location within the environment and second frame data (110) representing an image produced by the sensor at a second time and location within the environment. The method harmonises (206) information about the first frame data, the second frame data and an overlapping subset of the point cloud data in order to determine a location within the point cloud data where at least one of the first frame and the second frame was produced, thereby localising (208) the transportable apparatus within the environment.

Abstract: A method of localising transportable apparatus (100) within an environment including receiving (402) data obtained from a ranging sensor device (102) of the transportable apparatus configured to collect information relating to at least a surface (120) over which the transportable apparatus is moving in an environment, and using (404) the ranging sensor device data to generate a new 3D point cloud (110) of the environment. The method further obtains (406) data representing an existing 3D point cloud (114) of at least part of the environment, and seeks to match (408) the new 3D point cloud with, or within, the existing 3D point cloud in order to localise the transportable apparatus with respect to the existing point cloud.