Mobile Mapping

Top three positioning systems for mobile mapping vehicles

Purpose: Learn about positioning systems for mobile mapping vehicles
Last Updated: October 2024

Abstract

This article reviews three of the most effective positioning systems used in mobile mapping vehicles. The advantages and disadvantages of each system are analyzed, including satellite navigation systems (GNSS), inertial navigation systems (INS), and Simultaneous Localization and Mapping (SLAM). The article discusses the characteristics and accuracy of these technologies, as well as the specifics of their application in real-world conditions to create high-precision geospatial data.

Here are the sections that will be covered: Introduction to Mobile Mapping, Positioning system #1 GNSS, Positioning system #2 INS, Positioning system #3 SLAM and Mobile mapping solutions from Inertial Labs. The conclusion will summarize the benefits of using these technologies, as well as their combination and how Inertial Labs™ solutions can help you get accurate data with mobile mapping.

Section 1. Introduction to Mobile Mapping

Mobile mapping is a method of mapping using a data collection system and a moving vehicle, most often a car [1]. Typically, the data collection system is equipped with a GNSS receiver, an inertial navigation system (INS), and cameras and/or LiDARs, such as Google Street View, Figure 1 [2].

Figure 1. Google Street View car in Germany.

In mobile mapping, as in aerial mapping using unmanned aerial vehicles or drones, accuracy is important. The accuracy of each component of the data collection system will ultimately affect the accuracy of the maps. In addition, the calibration of the entire system is also important [3]. The data collection system must not only provide high accuracy, but also work reliably in places where the GNSS signal is unavailable or may be temporarily lost. Such places may include tunnels, dense forests, or urban areas with dense high-rise buildings.

If any of these aspects are neglected, the maps that will be created by such a system will have a large error or will be completely unusable. Therefore, it is important to know the position of the vehicle with high accuracy, considering the specifics of the terrain.

               Now a few words about data collection. Data collection is based on the combination of a camera, lidar or other scanners with an orientation and navigation system. The latter’s task is to determine the orientation of the scanner (yaw, pitch and roll), as well as its coordinates (latitude, longitude and altitude). The more accurately these parameters are determined, the more accurate the maps will be.

               Next, we will look at three of the most popular vehicles positioning technologies for mobile mapping.

 

Section 2. Positioning system #1 GNSS

Global Navigation Satellite System (GNSS) – technology is a collection of satellite positioning systems with worldwide coverage [4]. These systems aid in positioning, navigation, and timing (PNT) applications for commercial and military purposes.

As of 2024, the following constellations are functioning: GPS (United States’s Global Positioning System), GLONASS (Russia’s Global Navigation Satellite System), BDS (China’s BeiDou Navigation Satellite System) and Galileo (European Union’s Satellite System) [5]. In addition, regional systems are used: QZSS (This is a regional Japanese Quasi-Zenith Satellite System), NavIC (This autonomous system for the Indian region) [5].

GNSS consists of many satellites that orbit the Earth in fixed orbits. Each satellite constantly transmits radio signals containing information about the time of departure of the signal and the orbital position of the satellite. A receiving device (e.g., a smartphone or navigator) receives signals from several satellites at the same time. Information from at least four satellites is required for accurate positioning, more information can be found here [6]. Based on the time it took for the signal to travel from the satellite to the receiver, the system calculates the distance to each satellite. Using data from multiple satellites, the receiver can pinpoint its location by three coordinates (latitude, longitude, and altitude).

But GNSS has a few limitations that can affect its accuracy and reliability. Here are some of them:

  • Atmospheric distortions. GNSS signals can be distorted as they pass through the atmosphere, especially in the ionosphere and troposphere, which can reduce positioning accuracy. Although receivers consider the correction of the signal as it passes through the ionosphere and troposphere, there are cases where disturbances occur, such as geomagnetic storms, the accuracy of the location can deteriorate significantly [7].
  • Multipath propagation. When a signal bounces off buildings, mountains, or other objects, it can cause delays in its travel to the receiver, which in turn affects the accuracy of the calculations. As buildings surround the receiver, satellite signals can be reflected and refracted, sometimes repeatedly, before finally reaching the receiver, Figure 2.

Figure 2. Effects on the GNSS signal.

  • Insufficient number of satellites. In cities with tall buildings or in remote areas, there may not be enough visible satellites for accurate positioning, which is also shown in Figure 2.
  • Low power signals. GNSS signals are quite weak, and there may be difficulties in receiving them indoors, under dense covers, or in conditions of high electronic noise.
  • Security and vulnerabilities. GNSS can be subject to attacks such as jamming and spoofing, which can lead to failures in the navigation system [8].
  • Limited accuracy. Some applications (e.g., surveying) may require high accuracy that standard GNSS may not be able to provide without additional correction techniques.

 

In this regard, the GNSS system cannot be called reliable, so it is often integrated with an inertial navigation system.

Section 3. Positioning system #2 INS

An inertial navigation system is a device that uses gyroscopes and accelerometers (these are the main components that make up an inertial measurement unit – IMU), and sometimes magnetometers, to determine the orientation angles, position, and velocity of an object without external data sources [9].

The principle of operation of INS is that accelerometers measure the acceleration ax,y,z of an object along different axes (usually three mutually perpendicular axes – X, Y and Z). Gyroscopes measure the angular velocities of rotation ωx,y,z of an object relative to these axes, Figure 3. This allows tracking changes in the orientation of an object in space [10].

Figure 3. Location of gyroscopes and accelerometers in the IMU.

First, the acceleration measured by the accelerometers is integrated over time to determine the object’s velocity. Then, the velocity is integrated again to calculate the object’s current position in space. Signals from the gyroscopes are also integrated to determine the orientation angles (pitch, roll, and yaw).

Since inertial sensors are subject to errors (e.g., gyro drift and accelerometer noise), the system uses filters such as the Kalman filter to minimize errors and correct the readings [11]. To compensate for these errors, INS is coupled with GNSS to compensate for accumulated errors. However, the longer the GNSS signal is lost, the greater the errors will be. This is especially critical for long distances. Thus, INS is ideal for situations where autonomous navigation is required, but additional correction methods must be used to maintain its accuracy [12].

 

Section 4. Positioning system #3 SLAM

Simultaneous Localization and Mapping (SLAM) is a technology that allows autonomous systems to simultaneously build a map of an unknown environment and determine their location in this environment [13]. SLAM uses various types of sensors, such as LiDARs, cameras, or ultrasonic sensors. IMUs are used to determine the orientation of these sensors, so, as in the case of GNSS and INS, LiDARs/cameras, etc. are also integrated with INS, and GNSS is additionally integrated to calculate coordinates.

The principle of operation of the SLAM system is quite simple. Sensors collect information about the environment and the movement of the device. Lidars measure the distance to objects and create a point cloud, and cameras provide visual information (images), which are then analyzed to highlight key features (such as corners, contours, and other landmarks). It must be said that sometimes systems are made without cameras, receiving only a point cloud.

Localization involves determining the current position and orientation of a device in space based on sensor data. The system monitors changes in sensor data (e.g., changes in a point cloud or shifts in key points in images) to calculate its own movement. Algorithms such as the Kalman filter or particle filter are often used to minimize errors and smooth out noise in sensor data.

As the device moves, the system builds a map of the surrounding space using sensor data.

The main advantage of SLAM is its complete autonomy and ability to work in almost any conditions. In tunnels, cities, forests, etc., in the complete absence of a GNSS signal. This technology is also quite flexible, so it is easily integrated for various types of sensors.

But there are also disadvantages: when using SLAM, there is no georeferencing of data, i.e., the map is local. The ability to work is limited by weather conditions. SLAM requires large computing power to process data in real time, especially if lidars or high-resolution cameras are used.

 

               Thus, each of the technologies has its advantages and disadvantages. For mutual compensation, these technologies are often combined into one system. To summarize, we will provide a comparison table of all three technologies, table 1.

Table 1. Comparison of technologies for mobile mapping.

Technology

Advantages

Flaws

Notes

GNSS

Worldwide availability. High accuracy when using corrections.

Signal availability depends on the environment and external factors.

It is possible to obtain accuracy of about 1 cm but using RTK [14]. However, the disadvantages remain.

INS

High precision and autonomy. Ability to work in any conditions.

GNSS correction to reduce errors.

In addition to GNSS, Sensor can be used Fusion [15].

SLAM

High precision and autonomy.

Lack of geo-referencing data. Requires large computing power to process data in real time.

Not all systems operate in real time. Third-party software is required for georeferencing

 

Now let’s look at the opportunities you get with Inertial Labs products.

 

Section 5. Mobile mapping solutions from Inertial Labs

               The user can build his mobile mapping system using high-precision IMU, Figure 1 [16]. The KERNEL-210 and KERNEL-220 Inertial Measurements are the third generation of the Inertial Labs Miniature MEMS sensor-based family. The KERNEL-210 and KERNEL-220 are revolutionary, compact, self-contained, strapdown, Tactical-grade Inertial Measurement Units that measure linear accelerations and angular rates with precision due to their aligned and calibrated three-axis MEMS accelerometers and three-axis MEMS gyroscopes. Angular rates and accelerations are determined with low noise and very good repeatability for both motionless and dynamic applications.

Figure 4. Kernel-210 IMU.

Figure 5. Technical characteristics of KERNEL-210, KERNEL-220.

A wide range of inertial navigation systems are also available [17]. For example, Single and Dual Antenna GPS-Aided Inertial Navigation System – INS is a new generation of fully integrated, combined GPS, GLONASS, GALILEO, QZSS, BEIDOU and L-Band navigation and high-performance strapdown system, that determines position, velocity and absolute orientation (Heading, Pitch and Roll) for any device on which it is mounted. Horizontal and Vertical Position, Velocity and Orientation are determined with high accuracy for both motionless and dynamic applications.

Figure 6. The Inertial Labs Dual Antenna GPS-Aided Inertial Navigation System – INS-D.

The Inertial Labs INS utilizes advanced single and dual antenna GNSS receiver, barometer, 3-axes each of calibrated in full operational temperature range precision Fluxgate magnetometers, Accelerometers and Gyroscopes to provide accurate Position, Velocity, Heading, Pitch and Roll of the device under measure. INS contains Inertial Labs new on-board sensors fusion filter, state of the art navigation and guidance algorithms and calibration software.

 

In addition, the user can use a ready-made solution based on high-precision lidars [18]. The RESEPI (Remote Sensing Payload Instrument) GEN-II payload is an advanced, next-generation, sensor-fusion platform designed for accuracy-focused real-time and post-processed aerial, mobile, and pedestrian-based remote sensing applications, Figure 6. RESEPI GEN-II utilizes the Inertial Labs’ Dual Antenna Inertial Navigation System (INS-D), a high-performance and expandable navigation system powered by Inertial Labs’ Extended Kalman Filter (EKF). Within this system lies a Tactical Grade Inertial Measurement Unit (IMU), the Kernel-210, also by Inertial Labs. It provides expanding ability over its predecessor by offering end-users and integrators the ability to integrate their platforms by taking advantage of built-in software integrations made by MAVLink and DJI’s Payload SDK (PSDK). Benefit from two new camera options with a wider field of view, faster shutter speeds, and higher resolution images. Field-swappable mounts and accessories easily integrate with platforms like the WISPR Ranger Pro 1100, Freefly Astro, Sony Airpeak S1, and DJI M350.

Figure 7. RESEPI GEN-II.

The RESEPI GEN-II platform features a more powerful on-board computing module for real-time point cloud visualization and further integrations with external/additional sensing modules, giving users the ability to integrate and synchronize their additional cameras and LiDAR or input-aiding data to the navigation filter from wheel speed sensors, encoders, external IMU’s or Air Data Computers (ADC). This payload is perfectly suited for plug-and-play with end-users and engineering firms looking to adopt a hardware package that offers customization and expandability with a versatile remote sensing solution.

The accuracy of the system and the capabilities of the software are shown in Figure 7.

RESEPI technical characteristics GEN – II with XT-32M2X.

              

               Thanks to ready-made high-precision solutions, the user can create his own mobile mapping system. But if there is no desire to spend time on development and debugging, then RESEPI GEN – II is the best solution for mobile mapping.

Conclusion

The choice of a positioning system for mobile-mapping vehicles should be based on the specifics of the tasks and operating conditions. Satellite navigation systems (GNSS) and inertial navigation systems (INS) each have their own advantages, but their integration provides the highest accuracy and reliability of positioning. The combined use of GNSS and INS minimizes the disadvantages of each technology and adapts to various conditions, such as work in densely populated urban areas or in forested areas. Simultaneous technology also deserves attention Localization and Mapping (SLAM), which allows you to obtain terrain maps using a camera or lidar without using a GNSS signal. But the use of this technology is limited by weather conditions, and the data obtained in this way is not georeferenced.

Inexpensive and high-quality solutions from Inertial Labs play a key role in improving mobile mapping and improving the quality of geospatial data. Depending on the user’s wishes, both individual components are available, based on which he can create his own mobile mapping system, and a ready-made solution. High-precision IMU and INS will provide precise orientation and navigation even in the most difficult conditions. RESEPI ready-made solution GEN-II does not require additional costs and has unique integration capabilities.

Among the many developers, Inertial Labs™ stands out for offering customizable solutions that meet the unique needs of different applications. Their systems are designed to be easily integrated with a wide range of external sensors, allowing for flexibility that improves system performance and simplifies user integration. This approach not only simplifies development, but also significantly reduces the associated costs.

 

References

[1] Wikipedia Contributors. “Mobile Mapping.” Wikipedia, Wikimedia Foundation, 18 Dec. 2023, en.wikipedia.org/wiki/Mobile_mapping. Accessed 7 Oct. 2024.

[2] Wikipedia Contributors. “Google Street View.” Wikipedia, Wikimedia Foundation, 26 Apr. 2019, en.wikipedia.org/wiki/Google_Street_View.

[3] Mendez, Maria. “A Comprehensive Guide to Boresight and Strip Alignment for LiDAR Data Accuracy.” Inertial Labs, 16 Aug. 2024, inertiallabs.com/a-comprehensive-guide-to-boresight-and-strip-alignment-for-lidar-data-accuracy/.

[4] Wikipedia. “Satellite Navigation.” Wikipedia, 19 Mar. 2020, en.wikipedia.org/wiki/Satellite_navigation.

[5] Mendez, Maria. “An In-Depth Look at the Principles of GNSS.” Inertial Labs, 23 Aug. 2024, inertiallabs.com/an-in-depth-look-at-the-principles-of-gnss/. Accessed 25 Sept. 2024.

[6] Jan Van Sickle. GPS for Land Surveyors. Boca Raton, Crc Press, Taylor & Francis Group, 2015.

[7] “Space Weather and GPS Systems | NOAA / NWS Space Weather Prediction Center.” Www.swpc.noaa.gov, www.swpc.noaa.gov/impacts/space-weather-and-gps-systems.

[8] “Understanding the Difference between Anti-Spoofing and Anti-Jamming.” Novatel.com, novatel.com/tech-talk/velocity-magazine/velocity-2013/understanding-the-difference-between-anti-spoofing-and-anti-jamming#:~:text=Generally%20speaking%2C%20adversaries%20may%20attempt.

[9] Wikipedia Contributors. “Inertial Navigation System.” Wikipedia, Wikimedia Foundation, 21 May 2019, en.wikipedia.org/wiki/Inertial_navigation_system.

[10] Aboelmagd Noureldin. Fundamentals of Inertial Navigation, Satellite-Based Positioning and Their Integration. Heidelberg, Springer, 2013.

[11] Wikipedia Contributors. “Kalman Filter.” Wikipedia, Wikimedia Foundation, 27 Mar. 2019, en.wikipedia.org/wiki/Kalman_filter.

[12] Mendez, Maria. “Integrating INS with Aiding Data Technologies – the INS Ecosystem RoadMap.” Inertial Labs, 29 Apr. 2024, inertiallabs.com/integrating-ins-with-aiding-data-technologies-the-ins-ecosystem-roadmap/. Accessed 25 Sept. 2024.

[13] Wikipedia Contributors. “Simultaneous Localization and Mapping.” Wikipedia, Wikimedia Foundation, 8 July 2019, en.wikipedia.org/wiki/Simultaneous_localization_and_mapping.

[14] “Real-Time Kinematic Positioning.” Wikipedia, 5 June 2022, en.wikipedia.org/wiki/Real-time_kinematic_positioning.

[15] Mendez, Maria. “Inertial Labs Next-Generation Sensor Fusion Platforms.” Inertial Labs, 30 Oct. 2023, inertiallabs.com/inertial-labs-next-generation-sensor-fusion-platforms/. Accessed 7 Oct. 2024.

[16] “IMU – Inertial Measurement Units.” Inertial Labs, inertiallabs.com/products/imu-inertial-measurement-units/.

[17] “INS – GPS-Aided Inertial Navigation Systems.” Inertial Labs, 13 Aug. 2024, inertiallabs.com/products/ins-inertial-navigation-systems/.

[18] “RESEPI – LiDAR Payload & SLAM Solutions.” RESEPI, 12 July 2024, lidarpayload.com/.

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