LIO

  • 0.218 m Position 2D
  • 0.050° Heading
  • 0.020° (60 s GNSS outage)
  • 0.037 m/s Velocity 3D
TRIAL OxTS 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.

The OXTS LIO

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.

DOWNLOAD TECHNICAL PAPER

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.

Compatibility

Compatible LiDAR sensor families

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

Specification

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.

MeasurementGNSS OutageRMSE
Position 2D60 s0.218 m
Heading60 s0.050°
Roll/Pitch60 s0.020°
Velocity 3D60 s0.037 m/s
Altitude60 s0.128 m
Tools, utilities and add-ons

LiDAR Inertial Odometry features

  • 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.

Test data

01

Point cloud – Multi-storey car park

Bicester, UK – Point cloud – Multi-storey car park

  1. GNSS/INS – RT3000 v4
  2. LiDAR – Hesai XT32
  3. 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 point cloud screenshots above.

02

KML Trail – Urban Canyon

London, UK – KML Trail – Urban Canyon

  1. GNSS/INS – RT3000 v4
  2. LiDAR – Hesai XT32
  3. 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.

03

Pointcloud – Tunnel

San Francisco, USA – Pointcloud – Tunnel

  1. GNSS/INS – RT3000 v4
  2. LiDAR – Hesai XT32
  3. 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.

04

KML Trail – Multi-storey Car Park

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

  1. GNSS/INS – RT3000 v4
  2. LiDAR – Hesai XT32
  3. 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.

05

Pointcloud – Multi-storey Car Park

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

  1. GNSS/INS – RT3000 v4
  2. LiDAR – Hesai XT32
  3. Software: NAVsuite, OXTS LIO

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

06

Pointcloud – Urban Canyon

San Francisco, USA – Pointcloud – Urban Canyon

  1. GNSS/INS – RT3000 v4
  2. LiDAR – Hesai XT32
  3. 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.

Compatible INS Devices

Choose your hardware

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.

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Trial the beta version of LIO

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!

GET TRIAL VERSION Get in touch