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

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


ICPR 2020

technical paper

### 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!

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

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Review of Flight Trajectory Optimisation Taxonomy

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Rail Localization Integrity Concept using EGNOS extension

Lessons Learned from SBAS Systems and Testbeds towards Next Generation SBAS Services

Session 3.4.

Multi Sensor Fusion for Autonomous Space PNT

Session 3.5.

Position integrity for cooperative land vehicle networks

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

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