United Kingdom

In precision agriculture, vision-based systems are often used for machine steering. When a 3D
point cloud of the site exists and the machine is equipped with a stereo camera, the pose of
the machine can be estimated through Normal Distributions Transform (NDT). However, in
homogeneous environments with elongated features such as windrows at a composting site,
registration is difficult and large along-track errors occur. This is disadvantageous, especially
when a compost turner should be steered automatically along the windrows. This paper aims at
reducing the along-track error of the point cloud registration through multi-sensor fusion using
GNSS-RTK, odometry and a MEMS IMU in an error state Kalman filter. At first, the point cloud
registration algorithm is coupled with a dynamic model which uses odometer data to propagate
the vehicle state. Secondly, GNSS-RTK and IMU measurements are added. To evaluate both pro-
posed approaches, tests are carried out at a real composting site where sensor data are recorded
and later replayed for real-time simulation. The resulting position estimates are compared to
a highly precise ground truth to assess how the along-track error improves. The results show
that adding odometry helps to reduce the maximum along-track error. However, when NDT
fails and only odometry is used to propagate the state vector, the trajectory is affected by drift.
By adding GNSS-RTK and IMU observations, the along-track, cross-track and heading errors
are significantly reduced. The final multi-sensor solution is sufficiently accurate for machine
steering.

Navigation 2021

Reducing the along-track error of point cloud registration through multi-sensor fusion: a case study for machine steering at a composting site

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Our aim was to bring diverse disciplines together to stimulate insights and knowledge sharing towards a more navigable world. We hope we achieved this!

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https://d3smihljt9218e.cloudfront.net/transcript/30309/subtitle/2528baed9a32f1b8b0aab724b1d262c7.vtt

English (automatic)

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Reducing the along-track error of point cloud registration through multi-sensor fusion: a case study for machine steering at a composting site

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