Distributed large scale terrain mapping for mining and autonomous systems

This paper develops an information (inverse-covariance) based method for efficient fusion and distributed estimation of large scale terrain. The output resembles a standard triangulated irregular network (TIN) terrain representation. However the proposed method uses distributed information fusion to estimate the elevations of the mesh vertices. This terrain mapping system is intended to use multiple scanning vehicles for online monitoring of the terrain for automated mining operations or other multi-vehicle field robotics systems. The method is based on a pre-specified regular finite-element mesh to define the set of estimated state variables. The method maintains a joint Gaussian distribution of the mesh vertices' elevations, in the information (inverse-covariance) form. The mesh elevations are estimated jointly given the irregular terrain observations, together with smoothness terms. The smoothness terms enable interpolation into unobserved regions as well as reducing noise. In the information form, the observations and smoothness terms are additive and the information matrix remains sparse in a fixed pattern, enabling constant-memory fusion of observations, efficient distribution among multiple sensing platforms and efficient solving for the estimates and uncertainty. Results show the reduction in data size for the fused observations compared to the raw observations, whilst still obtaining large scale high quality terrain maps. This paper focuses on a hierarchical distributed system in which each node estimates a subset of its parent's region, with the top-level node estimating a terrain map of the whole area. This paper compares two methods for the distributed communication from parent to child: An exact but expensive method, and an approximate fast method. Results compare the communication cost and resulting level of estimation approximation, showing that the marginalised information is expensive and the approximation is acceptable without it. This paper is applied to the estimation of large scale surface terrain from a distributed network of multiple sensors, such as 3D laser scanners, for automated terrain mapping for large scale mining applications.