LiDAR Inertial Odometry (LIO)

OxTS manufacture cutting edge GNSS-aided Inertial Navigation Systems (INS). OxTS INS devices are used across the world in a multitude of applications where accurate and reliable localisation and groundtruth data is required.

Any navigation engine that uses GNSS to calculate position is susceptible to position drift and the longer the device is without GNSS signal, the more severe the drift becomes.

Position drift isn’t a challenge in open-sky conditions, however, in areas with poor GNSS visibility, such as urban canyons, ensuring position drift is kept to a minimum is critically important. To minimise the effects of position drift, other aiding sources, such as LiDAR, are required.

OxTS LIO, is an optional feature of OxTS INS devices that uses LiDAR sensor data to improve vehicle trajectory measurements. The enhanced measurements are then used to constrict position drift in the absence of GNSS.

OxTS LiDAR Inertial Odometry Technical Paper

Download the OxTS LIO technical paper and learn more about how the new feature works alongside OxTS INS devices.

The technical paper covers several key topics including…

How LIO works
Performance evaluations – Oxford and London
Potential applications and use cases
What is required to get the most out of LIO

How does LIO work?

LiDAR Inertial Odometry (LIO) uses LiDAR as an aiding source to the INS. Using LiDAR data alongside the navigation data from an OxTS INS mitigates the effects of position drift in sub-optimal GNSS conditions.

The feature combines the data from any LiDAR sensor natively available in OxTS Georeferencer, with the navigation output from an OxTS INS using the OxTS Generic Aiding Data (GAD) engine in post-processing.

The outcome is improved position accuracy in the absence of the ideal number of GNSS satellites for RTK accuracy.

Where can I use LIO?

In areas with good satellite visibility LIO will offer minimum benefit. As the optimum number of satellites will be in view, using an additional aiding source to achieve RTK accuracy should not be needed.

Where customers will witness most performance improvement is in environments where GNSS signal is difficult to obtain consistently, such as urban canyons.

A LiDAR sensor builds up a picture (digital replica or pointcloud) of its environment by emitting beams of light and measuring the time taken to return. Understanding the time it takes for the light to return, enables the sensor to calculate the distance to an object.

Therefore, built up areas, such as urban canyons, with flat planes, are perfect environments to get the most performance improvement from LIO.

London

Compatible LiDAR sensor families

The LIO feature code is compatible with many 360° field-of-view automotive grade LiDAR sensors, including those from:

Trial the beta version of LIO today and witness the performance improvement yourself!

Users of OxTS Inertial Navigation Systems can trial the beta version of LIO for free today!

Complete the form and one of our developers will be in touch to provide you with the software!

Alternatively, send us your data to info@oxts.com and we will process it for you!

Measurement	GNSS Outage	RMSE
Position 2D	60 s	0.218 m
Heading	60 s	0.050°
Roll/Pitch	60 s	0.020°
Velocity 3D	60 s	0.037 m/s
Altitude	60 s	0.128 m

Odometry accuracy – 0.03 – 0.05 m/s

Measurement rate – 5-20 Hz

The specification values here have been obtained statistically using a Hesai XT32 LiDAR device and an OxTS INS.

The data was collected in the city of Oxford using an RTK Integer reference dataset. Oxford was chosen as it closely resembles other urban areas. Please note that these values may vary depending on your LiDAR set-up and the environment.

Windows OS supported.
Feature code on the INS to enable.
Software running in post-process.

Ability to run from the command line or GUI.
Automatic quality report will generate after processing is complete to give debug outputs and performance metrics.

LIO GUI screenshots

The LIO is a feature available on all OxTS INS devices. To take advantage of the benefits of LIO you will require an OxTS INS, OxTS Georeferencer and the LIO feature.

The boresight calibration tool is optional. However, it is recommended that your payload is boresighted to maintain accuracy.

RT3000 v4

For when where matters most...

RT1003 v2

Ready for challenging GNSS environments...

AV200

Robust performance in an economical package - with ethernet...

xNAV650

Robust performance in an economical package
(Ethernet and Serial)...

xRED3000 – OEM board

for when competitive advantage matters...

Bicester, UK – Pointcloud – Multi-storey car park

GNSS/INS – RT3000 v4

LiDAR – Hesai XT32

Software: NAVsuite, OxTS LIO

Environment: In this example OxTS LIO was tested inside a multi-storey car park. Upon entering the car park the RT3000 v4 GNSS/INS naturally did not have any access to GNSS updates. To overcome this, velocity updates from a Hesai XT32 LiDAR sensor were used to aid the navigation engine.

The outcome is more accurate data as can be seen when comparing the before and after pointcloud screenshots below:

London, UK – KML Trail – Urban Canyon

GNSS/INS – RT3000 v4

LiDAR – Hesai XT32

Software: NAVsuite, OxTS LIO

Environment: In this example OxTS LIO was tested in one of the most difficult areas for a GNSS/INS to operate, the ‘Sky Garden Quarter’ urban canyon in London, UK. During the data collection, there were regular GNSS signal interruptions and severe multi-path disruptions. In this case, OxTS LIO was used to improve position drift making the data both accurate and repeatable throughout each lap of the data collection.

San Francisco, USA – Pointcloud – Tunnel

GNSS/INS – RT3000 v4

LiDAR – Hesai XT32

Software: NAVsuite, OxTS LIO

Environment: Even a short tunnel can present a major test for a GNSS/INS. The longer a GNSS/INS is without GNSS signal updates, the more pronounced position drift becomes. In this example, OxTS LIO was used to keep the navigation solution on track throughout its journey through the tunnel. Not only did this lead to improved navigation data but also a clearer pointcloud, as can be seen by viewing the pillars in the example below.

San Francisco, USA – KML Trail – Multi-storey Car Park

GNSS/INS – RT3000 v4

LiDAR – Hesai XT32

Software: NAVsuite, OxTS LIO

Environment: As mentioned previously, because GNSS signal updates are blocked in a multi-storey car park, a GNSS/INS device must use an additional aiding source to navigate accurately. In this example OxTS LIO has been used to improve the navigation output. This has led to a more consistent data set.

San Francisco, USA – Pointcloud – Multi-storey Car Park

GNSS/INS – RT3000 v4

LiDAR – Hesai XT32

Software: NAVsuite, OxTS LIO

Environment: During the data collection , the team also used the data to create a pointcloud of the multi-storey car park. The improvement OxTS LIO has made to the navigation data has subsequently improved the accuracy of the pointcloud.

San Francisco, USA – Pointcloud – Urban Canyon

GNSS/INS – RT3000 v4

LiDAR – Hesai XT32

Software: NAVsuite, OxTS LIO

Environment: The Mission Street urban canyon in San Francisco, USA is a street surrounded by glass-fronted high-rise buildings. GNSS signal is continuously interrupted and there is a danger of multi-path errors. During testing the RT3000 v4 by itself was very accurate, however when coupled with OxTS LIO it was almost seamless. OxTS LIO keeps the INS on a stable and consistent path through the city allowing it to filter out all multi-path signals.

Position 2D

0.218 m

Heading

0.050°

Roll/Pitch

0.020°

Velocity 3D

0.037 m/s

OxTS LiDAR Inertial Odometry Technical Paper

How does LIO work?

Where can I use LIO?

Compatible LiDAR sensor families

Trial the beta version of LIO today and witness the performance improvement yourself!

RT3000 v4

RT1003 v2

AV200

xNAV650

xRED3000 – OEM board

Bicester, UK – Pointcloud – Multi-storey car park

London, UK – KML Trail – Urban Canyon

San Francisco, USA – Pointcloud – Tunnel

San Francisco, USA – KML Trail – Multi-storey Car Park

San Francisco, USA – Pointcloud – Multi-storey Car Park

San Francisco, USA – Pointcloud – Urban Canyon