Top three positioning systems for mobile mapping vehicles
Mobile mapping is helping make some of the most ambitious technologies and projects of recent years a possibility. Digital maps created using scanners mounted on vehicles are helping autonomous vehicles know where to go, speeding up surveying projects for construction, improving the efficiency of conservation and public safety projects and even helping film and video game designers create accurate models of real-world locations for use in their work.
The number one concern therefore when mobile mapping, is of course, accuracy. If the maps that your work produces are inaccurate by even a small margin of error, it can make the end product unreliable at best, or unusable at worst. With this in mind, it’s particularly important that mobile mapping vehicle positioning is carefully considered for each project.
The way to map accurately is to combine a scanning element with technology that helps you precisely locate the vehicle conducting the scan, both on the earth and in relation to the world around it. If you know exactly where the vehicle is – down to the centimetre – then you can ensure that your maps/reports are accurate. Knowing values such as pitch and roll also helps you to ensure that your scanning apparatus remains accurate. This is particularly important over a long distance, protecting the integrity of the data.
The question is: how do you ensure your vehicles are accurately located? In this blog, we’re exploring a few different technologies that you can use in this endeavour. We also demonstrate the value of our favourite – a GNSS-aided inertial navigation system (INS).
Option One – GNSS only: You may choose to use a GNSS receiver on its own to calculate your position. For the uninitiated, GNSS stands for global navigation satellite system. A receiver is a hardware sensor that uses a group of satellites to work out its position. GPS is an example of one of the groups of satellites, also called constellations, that GNSS receiver technology can use. The others are GLONASS, BeiDou and Galileo.
A GNSS-only method can be accurate (to the centimetre) under the right conditions. Namely, in open skies with a clear line of sight to six satellites. However, GNSS struggles in locations such as dense tree cover, or urban canyons (large groups of tall buildings which restrict your view of the sky). This can make GNSS alone a rather unreliable method of getting an accurate position of your mobile mapping vehicle. On top of this, GNSS can’t give you data such as the roll or pitch of your vehicle. Without this information, if you’re surveying using a LiDAR, it’s very difficult to keep your scan data coherent as you scan over a large distance, limiting the usefulness of the data.
Option Two – SLAM: SLAM stands for simultaneous localisation and mapping, and is a method of mapping a space while also understanding your own location in that space. SLAM has been used in a variety of applications.
SLAM’s biggest limitation is that over time, unavoidable errors appear in the map and eventually the volume of errors grow to the point that the data is unusable for determining your location with absolute accuracy. This makes SLAM best used for small spaces such as indoors, or for helping autonomous cars park, where relative accuracy (understanding your position relative to a local reference frame rather than global) is the most important thing. Similar to our next solution, and unlike the previous, a SLAM solution does use pitch and roll measurements to compute accurate position, however, because it creates a map for you, it doesn’t output these measurements.
Option Three – INS: An INS, or inertial navigation system, uses a collection of sensors including accelerometers, gyroscopes, and sometimes a magnetometer (together these are known as an inertial measurement unit, or IMU) and a GNSS receiver to calculate its position. It uses the GNSS for positioning, with the IMU aiming to ensure position accuracy when the INS moves out of GNSS coverage by a process known as dead reckoning. The combination of sensors in the IMU also mean an INS can help a user understand a mobile mapping vehicle’s heading, pitch, and roll to give a very precise position. This is essential if surveying with a LiDAR.
Because an INS doesn’t rely solely on satellites, it can also perform well in an urban canyon or underneath a tree canopy. Additional technology, such as tight-coupling software, can improve the accuracy of your data even further. An INS with a tightly-coupled GNSS receiver and IMU can improve position drift which naturally occurs when a GNSS receiver loses satellite signal. OxTS’ gx/ix tight-coupling is one such technology that can help overcome position error in tough GNSS conditions.
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