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This blog is about GPS and inertial sensors for driverless cars. GPS is an essential technology for today's driving locations. However, due to the error, multi-path, and low update frequency of GPS, we cannot rely on it for positioning. Inertial sensors have a high update frequency and can be used in conjunction with GPS.

 

Catalog

I Self-driving car positioning technology

II Introduction to GPS

III Introduction to inertial sensors

IV GPS and inertial sensor fusion

V GPS vs inertial sensor & GPS vs   inertial sensor fusion

VI Conclusion

FAQ

I Self-driving car positioning technology

Driving location is one of the core technologies of Driverless cars. Global positioning system (GPS) also plays a very important role in driverless positioning. However, unmanned vehicles are driving in complex dynamic environments, especially in metropolitan areas, where GPS multipath reflections can be significant. This GPS positioning information is very easy to produce an error. Such errors are likely to cause traffic accidents for cars traveling at high speed over limited widths. Therefore, we must rely on other sensors to assist positioning and enhance the positioning accuracy. In addition, due to the low frequency of GPS update (10Hz), it is difficult to provide accurate real-time positioning when the vehicle is driving fast.

The inertial sensor (IMU) is a high-frequency (1KHz) sensor that detects acceleration and rotational motion. After the inertial sensor data is processed, we can get the displacement and rotation information of the vehicle in time. However, the inertial sensor itself also has the effect of deviation and noise . By using Kalman filter-based sensor’s fusion technology, we can integrate GPS and inertial sensor data to achieve better positioning results. Because unmanned driving’s requirements for reliability and safety are very high, positioning based on GPS and inertial sensors is not the only way to locate. We also match LiDAR with high-precision map, or position by visual odometer, so that a variety of positioning method will be adopted to correct each other in order to achieve more accurate results.

II Introduction to GPS 

Global Positioning System (GPS) is an indispensable technology for current driving location and plays a very important role in driverless positioning. The GPS system includes 32 GPS satellites in space, 1 master control station on the ground, 3 data injection stations and 5 monitoring stations, and a GPS receiver as a subscriber station. With at least three of these satellites, the location and altitude of the client on Earth can be quickly determined. Now civilian GPS can reach about 10 meters positioning accuracy. The GPS system uses low-frequency signals and maintains considerable signal penetration, even in poor weather. Following i will analysis GPS operating principle and technical flaws.

GPS three-way measurement of positioning

Figure 1. GPS three-way measurement of positioning

2.1 Trilateration method

As shown in Figure 1, GPS positioning system is the use of satellite basic triangulation Principle, utilizing GPS receiver to measure the transmission time of radio signals to measure the distance. From the location of each satellite, the distance between each satellite and the receiver can be measured to calculate the coordinates of the three-dimensional space of the receiver. Users receive the device as long as the use of three satellite signals received, you can set the user's location. In practice, GPS receiving devices use more than four satellite signals to locate the location and height of the user. Triangle positioning works as follows:

Assuming that we measure the distance of the first satellite to 18,000 km, we can limit the current range of possible locations to 18,000 km above the surface of the Earth from the first satellite.

Next, suppose we measure a distance of 20,000 km from the second satellite, and then we can further limit the current location to an intersection of 18,000 km from the first satellite and 20,000 km from the second satellite.

Then we will measure the third satellite again and locate the current position through the intersection of the three satellites. Normally, the GPS receiver uses the location of the fourth satellite to confirm the position measurements of the first three satellites for better results.

2.2 Distance measurement and precise time stamping

In theory, distance measurement is a simple process, and we only need to multiply the signal propagation time by the speed of light to get the distance information. But the problem is that the measured propagation time, any error, will result in a huge distance error. There is a certain amount of error in the clock we use every day. If we use quartz clock to measure the propagation time, there is a big error in GPS-based positioning. To solve this problem, atomic satellites are installed on each satellite to achieve nanosecond-level accuracy. In order for the satellite positioning system to use a synchronous clock, we need to have atomic clocks installed on all receivers as well. But atomic clocks cost tens of thousands of dollars, making it impractical for every GPS receiver to install such an expensive thing. In order to solve this problem, atomic clocks can still be used on every satellite, but ordinary quartz clocks often need to be calibrated at the receiver. Receivers receive signals from four or more satellites and calculate their own errors to adjust their own clock to a uniform time value.

2.3 Differential GPS

As mentioned above, there are problems such as errors caused by satellite clocks and delays in satellites' distance measurement. Using differential technology, we can eliminate or reduce these errors, so GPS to achieve higher accuracy. The principle of differential GPS operation is quite simple: if both GPS receivers are fairly close to each other, the signals from both will have almost the same error. If the error of the first receiver can be accurately calculated, The results of the two receivers are corrected.

Differential GPS

Figure 2. Differential GPS

How to accurately calculate the error of the first receiver? We can place the reference receiver reference station at a known and accurate location. As shown in Figure 2, the GPS receiver installed on the reference station can observe three satellites and perform three-dimensional positioning to calculate the measurement coordinate of the base station. Then we can calculate the error by comparing the measured coordinates with the known coordinates. The reference station then sends the error value to a differential GPS receiver within a radius of 100 km to correct their measurement data.

Multipath problem

Figure 3. Multipath problem

2.4 Multi-path problem

As shown in Figure 3, the multipath problem refers to the error of the signal propagation time caused by the reflection and refraction of GPS signals, which leads to positioning errors. Especially in urban environments, there are many suspended media in the air that reflect and refract GPS signals, and signals that reflect and refract on the outer walls of tall buildings, all of which cause confusion in distance measurements. The current high-precision military differential GPS, in the static and "ideal" environment can indeed achieve centimeter-level accuracy. The "ideal" environment here means that there is not too much suspended medium in the atmosphere, and the GPS has a stronger received signal when measured. However, unmanned vehicles are driving in a complex and dynamic environment, especially in large cities, GPS multipath reflections will be more obvious. This GPS positioning information is very easy to have a few meters of error, is likely to lead to traffic accidents.

Even with all sorts of problems, GPS is still a relatively accurate sensor, and GPS errors do not increase over time. However, one problem with GPS is the low update frequency, which is around 10Hz. Due to the speed of unmanned vehicles, we need real-time precise positioning to ensure the safety of unmanned vehicles. Therefore, we must rely on other sensors to assist positioning and enhance the positioning accuracy.

III Introduction to inertial sensors

The inertial sensor (IMU) is a sensor that detects acceleration and rotational movement. The basic inertial sensors include accelerometers and MEMS gyroscope. This article focuses on MEMS-based six-axis inertial sensors, mainly by the three-axis acceleration sensor and three-axis gyroscope components.

Here is a video introducing Inertial Sensor in detail:

Inertial sensor introduction

MEMS inertial sensors are divided into three levels: Low-precision inertial sensors are mainly used in consumer electronics products, smart phones, such sensors priced at 50 cents to a few dollars, but the measurement error will be relatively large. Intermediate inertial sensors are mainly used in automotive electronic stability systems and GPS-assisted navigation systems, such sensors priced at hundreds to thousands of dollars, relative to the low-end inertial sensors, intermediate inertial sensors in the control chip measurement error correction, So the measurement result is more accurate. However, after a long period of operation, the cumulative error will increase. High-precision inertial sensors as a military-grade and space-grade products, requiring high-precision, temperature zone, shock and other indicators. Mainly used for communications satellite wireless, missile seeker, optical aiming system and other stable applications. Such sensors are priced in the hundreds of thousands of US dollars range, even after a long run, such as transcontinental intercontinental missiles, still can achieve the rice level accuracy.

Unmanned aerial vehicles are generally low-level inertial sensors. It is characterized by high update frequency (1KHz), can provide real-time location information. But the fatal disadvantage of an inertial sensor is that its error increases over time, so we can only rely on inertial sensors for positioning in a short period of time.

Accelerometer

Figure 4. Accelerometer

3.1 Accelerometer

Figure 4 shows the MEMS accelerometer, which works by virtue of the inertia of the moveable part of the MEMS. Because of the large mass of the intermediate capacitor plate and its cantilever configuration, the inertial force it receives exceeds the force that holds or supports it when the speed or acceleration is large enough, at which point it moves, keeping it up and down The distance between the plates will change, the upper and lower capacitors will change accordingly. Capacitance changes with the acceleration is proportional to. Depending on the measurement range, the strength or spring constant of the cantilever structure of the intermediate capacitor plate can be designed differently. And if you want to measure the acceleration in different directions, the structure of this MEMS will be very different. Capacitor changes will be another piece of dedicated chip into a voltage signal, and sometimes the voltage signal will be amplified. The voltage signal is digitized and processed through a digital signal that is output after zero and sensitivity correction.

MEMS gyroscope

Figure 5. MEMS gyroscope

3.2 MEMS gyroscope

Figure 5 shows the MEMS gyroscope, which works on the principle of conservation of angular momentum. It is a non-rotating object whose axis of rotation does not change with the rotation of the support carrying it. Similar to the working principle of an accelerometer, the upper active metal of the gyroscope forms a capacitance with the underlying metal. As the gyroscope rotates, the distance between the gyro and the underlying capacitive plate changes, and the upper and lower capacitances change accordingly. The change in capacitance is proportional to the angular velocity, so we can measure the current angular velocity.

3.3 Inertial sensor problem

Due to the production process, inertial sensor measurements usually have some error. The first error is the offset error, ie, the gyroscope and accelerometer will have non-zero data output even without rotation or acceleration. To get the displacement data, we need to integrate the accelerometer's output twice. After two integrations, even a small offset error will be magnified and as time progresses, the displacement error will accumulate, ultimately resulting in no further tracking of the UAV's position. The second error is the ratio error, the ratio between the measured output and the change in the sensed input. Similar to the offset error, after two integrals, the error caused by the displacement will accumulate over time. The third kind of error is the background white noise that, if not corrected, can also prevent us from tracking the location of the UAV.

In order to correct these errors, we must calibrate the inertial sensor, find the offset error, the proportional error, and then use the calibration parameters to correct the original data of the inertial sensor. But the complication is that the error of the inertial sensor will also change with the temperature. Even if we make the best adjustments, as time goes on, the displacement error will continue to accumulate, so it is very difficult for us to use inertial sensors to locate UAV alone.

IV GPS and inertial sensor fusion

As mentioned above, GPS is a relatively accurate positioning sensor even with multi-path problems. However, the update frequency is low and can not meet the requirements of real-time calculation. The inertial sensor positioning error will increase with the running time, but because it is a high-frequency sensor, in a short period of time can provide stable real-time location updates. Therefore, as long as we find a way to combine the advantages of these two sensors, each director, you can get more real-time and accurate positioning. Below we discuss how to use the Kalman filter to fuse the two sensor data.

4.1 Introduction to Kalman Filter

Kalman filter predicts the position coordinates and velocity of an object from a set of observations that contain a limited set of noise-containing object positions. It has strong robustness. Even if there is an error in the observation of the object's position, we can accurately estimate the position of the object based on the historical state of the object and the current observation of the position. The Kalman filter is mainly divided into two phases: the prediction phase predicts the current position based on the position information of the previous time point; the update phase updates the position of the object by correcting the position prediction by observing the current position of the object.

To give a concrete example, suppose you have a power outage without any light and you want to walk back to the bedroom from the living room. You know the relative position of the living room to the bedroom, so you walk in the dark and try to predict the current position by counting steps. Halfway through, you touch the TV. Since you know in advance the approximate location of the television in the living room, you can correct your prediction of the current location by the location of your television set, and then continue to rely on the calculated steps based on the more accurate adjusted position estimate Several to the bedroom forward. Relying on the calculation of the number of steps and touch the object, you eventually dark from the living room back to the bedroom, the truth behind this is the core principle of Kalman filter.

GPS and IMU sensor fusion positioning

Figure 6. GPS and IMU sensor fusion positioning

4.2 Multi-sensor fusion

As shown in Figure 6, the fusion of inertial sensors and GPS data using a Kalman filter is very similar to the example given above. Inertial sensor here is equivalent to a few steps, and GPS data equivalent to the location of the reference TV. First of all, based on the last position estimation, we use the inertial sensor to predict the current position in real time. Before getting new GPS data, we can only predict the current position by integrating the data of inertial sensors. However, the positioning error of inertial sensors increases with runtime, so we can use this GPS data to update the current position prediction as new, more accurate GPS data is received. By constantly implementing these two steps, we can take the director of both to accurately locate the unmanned vehicle in real time. Assuming that the frequency of the inertial sensor is 1 KHz and the frequency of the GPS is 10 Hz, we can use 100 inertial sensor data points for position prediction between every two GPS updates.

V GPS vs inertial sensor & GPS vs inertial sensor fusion

This article describes the principle of using GPS and inertial sensors to accurately position a vehicle in an unmanned location. The system consists of three parts, a relatively accurate but low-frequency update GPS, a high-frequency update but increasingly unstable precision inertial sensors over time, and a Kalman filter-based mathematical model to fuse both Sensors, take the director, in order to achieve fast and accurate positioning effect. However, since driverless reliability and safety requirements are very high, in addition to GPS and inertial sensors, we often use positioning methods such as LiDAR and high-precision map matching, visual odometer and the like to make various positioning France correct each other in order to achieve more accurate results.

VI Conclusion

This article focuses on GPS and inertial sensors for driverless applications. GPS is an indispensable technology for current driving location.But due to GPS error, multipathing and low update frequency, we can not rely on GPS for positioning. The inertial sensor has a high update frequency that can complement with GPS. Using sensor fusion technology, we can integrate GPS and inertial sensor data in order to achieve better positioning results.


FAQ

 

1. What is GPS and its uses?

The Global Positioning System (GPS) has been developed in order to allow accurate determination of geographical locations by military and civil users. It is based on the use of satellites in Earth orbit that transmit information which allow to measure the distance between the satellites and the user.

 

2. What GPS means?

Global Positioning System. The Global Positioning System (GPS) is a U.S.-owned utility that provides users with positioning, navigation, and timing (PNT) services.

 

3. How does the GPS work?

GPS is a system of 30+ navigation satellites circling Earth. We know where they are because they constantly send out signals. A GPS receiver in your phone listens for these signals. Once the receiver calculates its distance from four or more GPS satellites, it can figure out where you are.

 

4.What is importance of GPS?

Why GPS is Important? GPS includes space-base satellites, computers and receivers which provide your location information in every weather conditions anywhere at any time in the world. It was originally made for the US military to locate their troops in deserted areas and forests.

 

5. How is GPS useful in our daily life?

Using GPS tracking systems, you can manage employee transportation fleet and improve its efficiency. You can save time and fuel, thereby minimizing expenses. While travelling, the feature in the GPS could track the luggage, laptop, and important personal belongings.

 

6. What is an IMU sensor?

An IMU is a specific type of sensor that measures angular rate, force and sometimes magnetic field. ... Technically, the term “IMU” refers to just the sensor, but IMUs are often paired with sensor fusion software which combines data from multiple sensors to provide measures of orientation and heading.

 

7. How does an inertial device work?

How Does an IMU Work? IMUs can measure a variety of factors, including speed, direction, acceleration, specific force, angular rate, and (in the presence of a magnetometer), magnetic fields surrounding the device. IMUs combine input from several different sensor types in order to accurately output movement.

 

8. How do you use the IMU sensor?

An IMU sensor unit working can be done by noticing linear acceleration with the help of one or additional accelerometers & rotational rate can be detected by using one or additional gyroscopes. Some also contain a magnetometer which can be used as a heading reference.

 

9. Why magnetometer is used in IMU?

The third component of our IMU is the magnetometer. This is where I have seen people facing difficulties. It is a device capable of measuring magnetism. It is able to help us find orientation using the earth's magnetic field, similar to a compass.

 

10. How do I choose an IMU sensor?

Some of the aspects we have to consider when we have to select an IMU are performance, underlying technology, SWaP (Size, Weight, and Power) and Cost. Besides, another important factor in UAVs is the ruggedness of the IMU. In harsh UAV applications, vibrations can reach a high level and different temperatures.

 

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