The requirement to estimate the six degree-of-freedom pose of a moving platform frequently arises in automation applications. It is common to estimate platform pose by the fusion of global navigation satellite systems (GNSS) measurements and translational acceleration and rotational rate measurements from an inertial measurement unit (IMU). This paper considers a specific situation where two GNSS receivers and one IMU are used and gives the full formulation of a Kalman filter-based estimator to do this. A limitation in using this sensor set is the difficulty of obtaining accurate estimates of the degree of freedom corresponding to rotation about the line passing through the two GNSS receiver antenna centres. The GNSS-aided IMU formulation is extended to incorporate LiDAR measurements in both known and unknown environments to stabilise this degree of freedom. The performance of the pose estimator is established by comparing expected LiDAR range measurements with actual range measurements. Distributions of the terrain point-to-model error are shown to improve from $0.27$$\mathrm{m}$ mean error to $0.06$$\mathrm{m}$ when the GNSS-aided IMU estimator is augmented with LiDAR measurements. This precision is marginally degraded to $0.14$$\mathrm{m}$ when the pose estimator is operated in an a prior unknown environment.

The capability to estimate the pose of known geometry from point cloud data is a frequently arising requirement in robotics and automation applications. This problem is directly addressed by Iterative Closest Point (ICP), however, this method has several limitations and lacks robustness. This paper makes the case for an alternative method that seeks to find the most likely solution based on available evidence. Specifically, an evidence-based metric is described that seeks to find the pose of the object that would maximise the conditional likelihood of reproducing the observed range measurements. A seedless search heuristic is also provided to find the most likely pose estimate in light of these measurements. The method is demonstrated to provide for pose estimation (2D and 3D shape poses as well as joint-space searches), object identification/classification, and platform localisation. Furthermore, the method is shown to be robust in cluttered or non-segmented point cloud data as well as being robust to measurement uncertainty and extrinsic sensor calibration.

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Autonomous mining machines must maintain an accurate model of the terrain they work in to support material movement planning and enable autonomous excavation. Terrain mapping is particularly challenging as these machines continuously modify the terrain they work in, often in the presence of other machines that simultaneously transform the same environment. This paper focuses on developing a capability to accurately create and maintain terrain maps that quickly reflect changes in the environment. The proposed approach extends the well-known height grid terrain representation to include a hidden Markov model per cell, allowing for confidence-based mapping of the constantly changing terrain that is robust to sensor noise. Hidden Markov models provide a probabilistic framework for handling uncertain observations and fusing sensor information to estimate the terrain height. The performance of the proposed mapping approach is evaluated on two field datasets: (i) an instrumented hydraulic excavator loading blasted ore into trucks, and (ii) a track-type tractor stacking material in a coal stockyard. The evaluation demonstrates the ability to accurately update the terrain estimate using the machine’s pose and point cloud data captured from machine-mounted LiDARs. The significance of this work is its capacity to provide real-time and accurate maps of changing terrain to support autonomous excavation.