Abstract
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 mean error to when the GNSS-aided IMU estimator is augmented with LiDAR measurements. This precision is marginally degraded to when the pose estimator is operated in an a prior unknown environment.
Highlights
The rationale for using the two sensor types is that global navigation satellite system (GNSS) receivers provide low-frequency information while the inertial measurement unit (IMU) provides information about higher frequency motion, noting that when stationary, the IMU measures gravity which can be considered as zero-frequency (DC) information
The main result from this work is to show how LiDAR can be used to stabilise twoantenna GNSS-aided IMU pose estimators in environments with known and unknown geometry
Lost measurements are a characteristic observed on some brands of real-time kinematic (RTK)-GNSS receivers, and the solution described in this paper adapts well to this situation
Summary
Measurements from the two sensors are combined using optimal estimation methods, vis-à-vis Kalman filtering, to maintain an estimate of the pose of the platform frame P as it moves relative to a global frame G. Each GNSS receiver provides positional knowledge of its antenna centres to a precision of approximately 0.05 m at a bandwidth from 0 Hz to 2–5 Hz (with measurement updates typically provided at 10 Hz). Higher frequency motion is determined from information provided by the IMU, which will typically provide acceleration and angular rate information from 0 Hz to 50 Hz, at update rates of 50 Hz to 100 Hz. Higher frequency motion is determined from information provided by the IMU, which will typically provide acceleration and angular rate information from 0 Hz to 50 Hz, at update rates of 50 Hz to 100 Hz Such an arrangement provides a nice division of labour, with the Kalman filter-based pose estimator serving to combine the disparate frequency information provided by the two sensor types
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