LiDAR is a famous technology for realizing autonomous driving in automobiles. However, did you know that due to its accuracy, LiDAR is being utilized in many industries beyond just the automotive sector?
While LiDAR is becoming a familiar technology, many people may not know the specific methods of utilization or implementation procedures. LiDAR is expected to become even more widespread in the future, making it necessary to understand its mechanisms and challenges.
In this article, we explain the mechanisms, use cases, challenges, and implementation procedures of LiDAR. Even if you are unfamiliar with LiDAR, you will be able to understand everything from the overview to the flow of introduction, so please use this as a reference.
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【Table of Contents】 |
LiDAR (Light Detection and Ranging) is a sensor technology that uses laser light to measure the distance and shape of objects. Unlike photography with a camera, LiDAR measurement involves emitting laser pulses at a target and receiving the reflected light with a sensor to calculate the distance to the object.
Laser pulses are laser beams that move at high speeds, making it possible to measure the distance to a target in a short amount of time. Since the surrounding environment can be acquired as high-density point cloud data, high-precision 3D measurement is possible.
Compared to traditional surveying techniques, large-scale data can be collected in a short time, significantly improving work efficiency. Furthermore, as a remote sensing technology, it is possible to safely acquire data even in hard-to-access areas.
LiDAR is also used for wide-area measurements such as terrain data acquisition, urban planning, and environmental monitoring.
The evolution of LiDAR is bringing innovation to various industries. In particular, automated recognition technology combining AI and LiDAR is being developed as an essential technology for autonomous driving, expanding into new fields of application. It is often said that the realization of autonomous driving technology depends on the accuracy of LiDAR.
Through high-precision distance measurement technology, LiDAR is capable of measuring the following types of data:
The fundamental function of LiDAR is measuring the distance to a target. It emits a laser pulse and measures the time it takes to reflect off a target and return, utilizing that data for reflection intensity, 3D coordinates, and dynamic object measurements.
Reflection intensity is effective for measuring the material and surface condition of a target, which is helpful, for example, in identifying road surfaces or types of vegetation. 3D coordinate data is used for mapping terrain, buildings, and other structures three-dimensionally, playing an important role in urban planning, infrastructure development, and disaster prevention.
Additionally, it is possible to measure the position and speed of dynamic objects, such as moving vehicles or pedestrians, in real-time. This measurement data is indispensable for autonomous driving technology.
These types of data measurable with LiDAR are utilized across various fields.
There are two measurement methods used by LiDAR: the TOF method and the FMCW method. Each method has its own characteristics, and the appropriate method must be chosen according to the application.
The TOF (Time of Flight) method is a measurement method that emits a laser pulse at a target and measures the time it takes for the reflected light to return to the sensor. By using short radar pulses, high-precision distance measurement is possible.
Additionally, because many data points can be acquired in a short time, it is suitable for real-time data processing. It is the general method for LiDAR used in various fields such as autonomous driving, drones, and surveying.
The FMCW (Frequency Modulated Continuous Wave) method is a measurement method that emits a laser with a continuously changing frequency and measures the frequency shift of the reflected light. The distance is calculated based on the measured frequency shift.
Because the FMCW method measures frequency changes with high precision, it is possible to accurately measure the distance to a target. Furthermore, compared to the TOF method, it is less susceptible to the surrounding environment and can identify reflected light even in weather conditions such as rain or fog.
The TOF and FMCW methods use different types of radar, and each has its differences. It is important to choose which method to use based on the application and environment. By utilizing the optimal LiDAR method, you can achieve maximum results.
While LiDAR is a technology for measuring 3D data, annotation for LiDAR data is indispensable for achieving automatic recognition with AI based on that data. Annotation is the task of attaching labels to acquired data to clarify the meaning of the data.
The acquired LiDAR data is massive, generating large volumes of point cloud data. Without labeling, it cannot be utilized as effective data. For example, to realize autonomous driving, it is necessary to accurately measure road conditions; through annotation, AI can recognize objects more accurately and make appropriate judgments.
Therefore, the utilization of LiDAR requires training with AI machine learning models, streamlining data management, and improving measurement accuracy. By performing high-quality annotation, it becomes possible to realize improved accuracy in automatic recognition utilizing LiDAR.
Since the accuracy of annotation directly affects recognition accuracy, it can be called a vital process. When considering the introduction of LiDAR, it is recommended to utilize annotation tools or companies compatible with LiDAR data.
LiDAR technology is mainly utilized in the following situations:
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Let's look at each use case.
In autonomous driving, for which technological development has progressed in recent years, LiDAR can be called an essential technology. LiDAR sensors can map the surrounding environment of a vehicle in real-time, accurately detecting other vehicles, pedestrians, obstacles, etc., to realize smooth driving.
In current advanced driver-assistance systems, millimeter-wave radar and cameras are often utilized. While these technologies can detect leading vehicles and lanes, they are not proficient at detecting shapes and positional relationships. On the other hand, LiDAR can acquire the surrounding environment as high-resolution 3D data, accurately grasping the shapes and positional relationships of objects. Consequently, automatic recognition technology utilizing LiDAR detects the positional relationships and shapes of people, objects, and buildings to accurately grasp constantly changing road conditions.
If LiDAR research and development continue, autonomous driving will likely become possible even in urban areas or narrow roads without lane markings. In particular, LiDAR's high-precision environmental recognition capability is indispensable for realizing advanced autonomous driving systems of Level 3 or higher.
LiDAR is also highly valued in the fields of surveying and mapping. By mounting LiDAR sensors on aircraft or drones, large areas of terrain and buildings can be scanned with high precision. This technology has made it possible to create maps of large-scale areas that were difficult with traditional surveying methods.
3D data from LiDAR can be integrated into Geographic Information Systems (GIS), making it useful for urban planning and infrastructure management. Because LiDAR provides accurate data in a short time, it significantly improves the efficiency and accuracy of surveying operations. In this way, LiDAR is attracting attention as a wide-area surveying and mapping tool.
LiDAR is also being utilized as an effective tool for the management of farmland and forests. By using drones equipped with LiDAR sensors, the condition of vast farmland and forests can be scanned to acquire 3D data. This data allows for the monitoring of crop status, growth conditions, and changes in land terrain.
Because farmland and forests boast vast areas, management by visual inspection is difficult. Therefore, management and monitoring utilizing LiDAR solve labor shortages.
In recent years, LiDAR sensors have also come to be installed in tablet devices such as iPhones and iPads. This makes it possible for general users to easily utilize LiDAR technology.
For example, there are utilization methods such as performing 3D scans of rooms with LiDAR to simulate interior design or furniture placement. In the real estate industry, by creating detailed 3D models of properties and providing virtual spaces, more realistic property information and experiences can be provided.
Furthermore, it is utilized in the entertainment and education sectors. By combining AR (Augmented Reality) technology with applications, it is also expected to provide new gaming experiences that merge the real world and virtual spaces.
Development of autonomous vehicles and robots is also progressing through LiDAR technology. LiDAR radar measures and scans the surrounding environment, supporting the autonomous navigation of vehicles and robots.
For example, when utilizing robots in warehouses or factories, LiDAR enables high-precision navigation and obstacle avoidance. This streamlines logistics operations and realizes the resolution of labor shortages and automation of tasks. It is also utilized in diverse environments such as agriculture and construction sites.
There are points to be noted when introducing LiDAR technology. Here, we explain the respective precautions for the TOF and FMCW methods, as well as the challenges for the practical application of LiDAR technology.
In particular, the utilization of multimodal sensor fusion and deep learning algorithms is seen as promising. Through the advancement of the above technologies, the realization of more reliable autonomous driving systems is expected in the future.
While the FMCW method is a LiDAR technology that emits radar with changing frequencies, its high cost compared to the TOF method has become a bottleneck.
The measurement method of the FMCW method is complex, requiring software to enhance the accuracy of frequency and signal processing. Furthermore, high-performance optical components, such as high-precision optical parts required for coherent detection, are costly. Consequently, introduction incurs high initial costs.
Manufacturing and introduction costs are higher than those of the TOF method, and preparing large quantities as for autonomous driving technology would be a major burden.
However, along with technological progress and market expansion, costs have recently been decreasing. In particular, demand for FMCW LiDAR is expected to increase with the growth of the autonomous vehicle market, and cost reduction through mass production effects is anticipated. Furthermore, due to advances in silicon photonics and manufacturing processes in the semiconductor manufacturing sector, the possibility of cost-competitive products appearing in the future is increasing.
Nevertheless, the penetration rate of products is still low at present, and cost-related challenges must be cleared to introduce the FMCW method.
Coherence length refers to the distance over which a light wave propagates while maintaining a constant phase relationship. This coherence length significantly affects the accuracy of LiDAR and is considered a problem for practical application.
If the coherence length is short, the coherence of the laser light becomes low, limiting the measurement distance. This makes it difficult to accurately detect distant objects or fine structures.
To address the coherence length issue, the following latest technologies are being researched and developed:
To introduce LiDAR technology into your company, proceed with the following 3 steps:
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We explain each step below.
First, you must clarify the purpose of introducing LiDAR. To ensure that the introduction of LiDAR technology does not become the goal itself, decide which business operations it will be utilized in and what you want to achieve through its introduction.
Objective optimization also requires consideration of your company's resources and the challenges and risks associated with LiDAR introduction. To formulate a realistic introduction plan, it is necessary to understand not only the usage scenes and goals to be achieved but also the internal resource allocation and challenges for realizing them.
By optimizing the purpose of introducing LiDAR based on these factors, the subsequent product selection will be able to proceed smoothly.
Once the purpose of introducing LiDAR is clarified, research and compare manufacturers' products. Since LiDAR systems are advanced technologies, they are typically outsourced.
To select an appropriate LiDAR system, it is important to thoroughly understand the characteristics of each manufacturer and the products they provide. In manufacturer product research and comparison, check items such as the following:
Furthermore, if possible, request demonstrations or trials from manufacturers. By actually trying the products, you can confirm operability and performance accuracy. You can directly compare the usability of products through demonstrations.
To introduce LiDAR, you must integrate it with existing systems. By formulating an integration plan for the introduction of selected manufacturer products, you can maximize the utilization of LiDAR technology and achieve smooth operation.
The formulation of an integration plan can proceed through a flow such as the following:
Basically, it is recommended to introduce products with high consistency with existing systems. Changing existing systems to match a LiDAR system may significantly alter business operations, potentially confusing employees.
Additionally, an annotation plan for LiDAR data is extremely important for effectively utilizing LiDAR data. By planning in advance everything from the execution of annotation and quality assurance to the integration and utilization of data, you can improve the accuracy and efficiency of annotation.
Once a specific and realistic integration plan is formulated, you will execute it and actually proceed with the introduction.
There are various use cases for LiDAR, and if accuracy improves and introduction costs are reduced, the fields of application will expand further. Therefore, those interested in LiDAR technology should know the methods, challenges, and introduction procedures introduced in this article as part of their knowledge.
Since the introduction of LiDAR and the subsequent automatic recognition based on the data obtained require personnel who can operate specialized techniques such as annotation at a practical level, you should generally outsource these tasks.
While more specialized knowledge is required for actual introduction, it is important to first understand the general flow. We hope this article deepens your understanding of LiDAR.