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Navigation 2021

Deep-Learning-Based Position Error Estimation with Uncertainty Quantification for Satellite-Based Localization

### Welcome to Navigation 2021!

Our aim was to bring diverse disciplines together to stimulate insights and knowledge sharing towards a more navigable world. We hope we achieved this!

Navigation and timing are critical to the world we all inhabit. We believe that the work of the wider navigation community enables societal benefit including support to national grand challenges and also the United Nations’ Sustainable Development Goals. To achieve this requires collaboration and understanding across science, technology and practice.

Navigation 2021 is about much more than technology innovation, it also showcases science and research from across the wider community, including animal navigation, cognition and human factors.


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

technical paper

Error Assessment of Deep Learning Based Lane Detection

session 2.6.

Protection of GNSS-based Synchronization in Communication Networks: The ROOT Project

40 years of PNT

Session 3.1.

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.

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

Using Reflected GNSS Signals to take Climate and Weather Measurements from Space

Session 3.2.

The National Space Strategy and Development of the National PNT Strategy

A new EU Space Programme and new Agency

live session recording

keynote

https://d3smihljt9218e.cloudfront.net/transcript/30310/subtitle/06b3c3cc394b2291b3efbcd9d0cf1e9d.vtt

English (automatic)

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Deep-Learning-Based Position Error Estimation with Uncertainty Quantification for Satellite-Based Localization

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