Abstract: A method (1300) of generating a training dataset for use in autonomous route determination, the method comprising obtaining (1302) data from a data collection vehicle (10) driven through an environment. The data comprises vehicle odometry data detailing a path taken by the vehicle (10) through the environment, obstacle sensing data detailing obstacles detected in the environment; and images (106) of the environment. The method (1300) further comprises using (1304) the obstacle sensing data to label one or more portions of at least some of the images (108) as obstacles and using (1306) the vehicle odometry data to label one or more portions of at least some of the images (108) as the path taken by the vehicle (10) through the environment.

Abstract: A neural network comprising at least one layer containing a set of units having an input thereto and an output therefrom, the input being arranged to have data input thereto representing an n-dimensional grid comprising a plurality of cells; the set of units within the layer being arranged to output result data to a further layer the set of units within the layer being arranged to perform a convolution operation on the input data; and wherein the convolution operation is implemented using a feature centric voting scheme applied to the non-zero cells in the input to the layer.

Abstract: A method and system for detecting objects within a three-dimensional (3D) environment, comprising obtaining a 3D point-cloud representation of the environment, the point-cloud comprising a set of point locations, and converting the point-cloud to a 3D feature grid, wherein converting the point-cloud to the 3D feature grid comprises discretizing the 3D space into cells according to a grid size; determining which cells contain points from the 3D point-cloud; and mapping any that do to a feature vector; and mapping any that do not to a zero feature vector.

Abstract: A method and system for detecting objects within a three-dimensional (3D) environment, comprising obtaining a 3D point-cloud representation of the environment, the point-cloud comprising a set of point locations, and converting the point-cloud to a 3D feature grid, wherein converting the point-cloud to the 3D feature grid comprises discretising the 3D space into cells according to a grid size; determining which cells contain points from the 3D point-cloud; and mapping any that do to a feature vector; and mapping any that do not to a zero feature vector.