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Blog Summary:
This blog is an in-depth guide for entrepreneurs, business owners, and startups looking to build a high-quality AI and AR-driven LiDAR app. It will provide them with valuable insights into the types of sensors, features, and the tech stack required to bring their app faster to the market.
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Have you ever wondered how the virtual world has opened up a treasure trove of opportunities for us to touch any part of the world? Every day, we are fortunate enough to know what a parallel universe would look like while existing in the real world.
The technology that can augment reality and make it virtual has truly passed the test of time. Its speed is almost equal to the speed of light—in a matter of seconds and sometimes even nanoseconds.
Laser beams have existed since the 1969 Apollo XI mission led by Neil Armstrong. The mission measured the distance a laser beam travels from Earth to the Moon through reflectors placed on the Moon. Known as the Lunar Distance 54 years ago, it is now known as Light Detection and Ranging (LiDAR).
Today, LiDAR App Development combines forces with augmented reality, virtual reality, artificial intelligence, and machine learning across various industries. In this blog, we’ll uncover more about how LiDAR uses the time-of-flight technology to build 3D environments.
LiDAR stands for Light Detection and Ranging, a remote sensing method for measuring an object’s distance on the earth’s surface. Your mobile devices act as cameras to emit tiny beams of light on nearby objects, allowing you to see everything in 3D.
Infrared lights measure the distance that light travels from the time it bounces off the surface and returns. In recent years, it has become a popular technology for developing LiDAR apps that help generate accurate data for building 3D maps.
Like X-ray vision, it helps you make detailed maps of the Earth, study the atmosphere, see obstacles, and navigate safely.
LiDAR works on the concept of pulsed lasers to measure the distance, shape, and size of the objects. Pulsed lasers emit light beams in nanosecond flashes, even up to 1,50,000 per second, to produce high-resolution images. LiDAR creates its light source, allowing it to work even in complete darkness to produce three-dimensional and 360-degree.
LiDAR consists of three major elements: a laser, a scanner, and a GPS receiver. Sensors that emit laser beams can be mounted on terrestrial or airborne vehicles. The time a laser signal takes to return to the sensor calculates the distance of the object.
It can be explained as the Distance of an object = time of flight x speed of light / 2
The precision and accuracy of its measurement make it ideal for real-time LiDAR apps, high-resolution digital elevation models (DEMs), and 3D maps of geological sites, geographical locations, dense forests, archaeological sites, and seismology.
The types of LiDAR sensors are based on how they collect, interpret, and process data depending on the speed at which they measure the distance of the light. LiDAR determines distances and creates 3D point clouds by sending waves and light beams from the sensors using time-of-flight.
Let’s understand the different types:
Flight-based or airborne LiDAR sensors are placed on aircraft used as laser scanners that emit light pulses to the ground. They are further divided into bathymetric LiDAR for measuring waterbody depths and topographic LiDAR for mapping regions.
In contrast, terrestrial LiDAR sensors work on the ground to scan objects from different directions. Examples include vehicle identification, highway and rail surveys, and 3D city replicas.
Modulation-based sensors work on sending continuous laser signals instead of pulses or flashes. There are two sophisticated sensors – Amplitude Modulation Continuous Wave (AMCW) and Frequency Modulation Continuous Wave (FMCW).
In AMCW, the beam changes the intensity in a specific pattern when it returns, and LiDAR measures the change in the amount of intensity. Common applications include mapping, surveillance, robotics, and autonomous vehicles.
In FMCW, LiDAR works by measuring the shift in the phase between emitted and received signals. Common examples include weather monitoring and self-driving vehicles.
Static LiDAR sensors collect data from static locations. Common applications include archaeology and mining. Meanwhile, their mobile counterparts are placed in moving vehicles to acquire data from millions of 3D data points per minute. Common applications include highly detailed 3D maps.
Improve data accuracy by integrating LiDAR sensors with your business processes.
LiDAR offers numerous advantages over traditional sensing technologies in terms of accuracy, precision, and speed. All these characteristics make it ideal for use in a lot of applications and domains, including agriculture, defense and military, self-drive vehicles, archaeology, and many more.
Let’s understand its pros in detail:
Higher accuracy in 3D maps allows you to measure distances better, as they capture even the subtlest shadows and changes in surroundings. LiDAR apps can detect objects in vertical and horizontal dimensions of 5-15 cm and 30-50 cm, respectively, enabling easy evaluation of surface, ground, and intermediate terrains.
LiDAR can deliver high performance in any condition, whether fog, rain, or snow. Unlike traditional sensors that might be affected by extreme weather, LiDAR can adapt itself for short—or long-range operations.
LiDAR is highly automated, making it more efficient for surveying operations than GPS or land surveying, which require humans. It can collect data from even the most remote places, such as the North Pole and South Pole, dense forests, high mountains, and deep water bodies.
Since LiDAR doesn’t require physical contact with the target object, it’s non-destructive and can be placed in delicate environments. This allows it to acquire rich data and valuable information about objects’ reflectivity, color, and structure.
Organizations are increasingly adopting laser technology to create affordable and high-performing LiDAR apps across different industries. Pulsed, airborne, and terrestrial LiDAR, along with advanced algorithms, brings higher resolution and better accuracy to mobile and web applications.
By allowing owners to make precise calculations from thousands of data points, LiDAR enhances the operations of advanced driver assistance systems (ADAS) and analyzes traffic environment patterns.
Many industries, such as agriculture, geology, telecommunications, automotive, construction, etc., can utilize GIS with LiDAR systems to build detailed indoor and outdoor maps, 3D scans of surroundings, and replicas.
Accurate data provided by LiDAR apps for real-life object placements helps bring precision to mapping the surroundings and building immersive environments with AR. Education training, medical procedures, and gaming industries can hugely profit from these.
LiDAR enables robots to map their surroundings, locate themselves, avoid obstacles, and perform tasks like picking up objects. This makes robots more autonomous and efficient in various environments, including warehouse automation, service robots, agriculture, etc.
LiDAR’s detailed 3D data makes environmental monitoring for disaster management more efficient. It addresses many challenges related to natural disaster assessment, habitat and ecosystem mapping, climate change monitoring, and infrastructure evaluation.
LiDAR apps work tremendously with Point Cloud technology in creating detailed 3D maps. By processing data collected through several cloud points, LiDAR extracts meaningful information that can be combined with other sensors. Here’s how they both deliver great user experience:
LiDAR collects real-time data from the individual cloud points of a 3D structure and provides information about the light intensity, coordinates, and time of flight of laser beams.
LiDAR filters the noise and uses feature extraction algorithms to identify, segment, and classify regions of interest. It then converts point clouds into highly accurate wireframes with GPUs.
A LiDAR app enhances the functionality of cameras, depth cameras, GPS, temperature sensors, and inertial measurement units (IMUs).
LiDAR-based apps provide better cloud connectivity for real-time data processing, analysis, and streaming. Integration with ML and data analytics tools allows easy scaling, management, and organization of large datasets.
Developing a LiDAR-based app requires a solid understanding of the hardware, software, and sensors that need to be integrated. A LiDAR app development company can help you figure out how to build a robust app.
Here are some things to consider:
Consider using a GPU to accelerate processing tasks like 3D visualization and point cloud. C++, JavaScript, and Python with frameworks like Three.js will help you build a software development kit (SDK) and a suitable integrated development environment (IDE).
For cloud data processing, AWS services like EC2, S3 Lambda, Google Cloud Platform (GCP), and Microsoft Azure work best. Point Cloud Library (PCL), LAStools, Open3D, and Point Data abstraction Library (PDAL) will offer a wide range of services.
ML models enhance the capabilities of LiDAR-based applications with object recognition and data analysis for better integration. Consider using ML models like convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and graph neural networks (GNNs).
LiDAR data and software have various applications for creating 3D maps and identifying features. Ensure the software can handle large datasets, offer efficient processing, and provide visualization tools. Here are some steps to follow:
Choosing the right LiDAR software depends on your specific needs and data requirements. Hence, define what you want your LiDAR app to achieve and how it will satisfy the needs of the people who will be using it.
Plan the structure of a LiDAR app by choosing the right sensors in terms of accuracy, range, and budget. Ensure the design and architecture are suitable and can be easily integrated with external development platforms.
Write and develop the right code to capture the data generated by LiDAR sensors to ensure that it’s stored in a format that can be easily processed and visualized. Use filtering, extraction, and object recognition algorithms to develop a user interface for 3D cloud points.
Before ensuring that the app performs accurately in real-world conditions, you can also opt to integrate LiDAR data with a camera and GPS for accurate calibration. Test the interface you built for different scenarios and devices to ensure that it is user-friendly and bug-free.
Gather user feedback before launching the app on relevant platforms (iOS, Android, web). A LiDAR app development company will help you maintain the app by continuously monitoring performance and addressing vulnerabilities.
Developing a LiDAR app requires a lot of smaller configurations to sync all the devices and interpret data better, which can pose challenges. Here are some of them and how to overcome them:
The standard frame rate produced by optical sensors is 30 frames per second (FPS). LiDAR generates 50ms for every new frame, which is too low to represent data in dynamic environments.
Solution: Consider using multiple sensors to gather data and merge the received output to get higher frame rates. You can also use ML algorithms to predict frame rates.
Poor scanning of black objects can often corrupt the point cloud in a map. Black paint absorbs a lot of light, which can hinder the performance and operation of autonomous vehicles.
Solution: Consider getting supplement sensor inputs like camera and radar to provide additional lighting and fill missing areas.
LiDAR processes and analyzes large datasets, which requires filtering and cleaning to get accurate results. For real-time applications, it can cause high latency and significant memory usage.
Solution: Consider using a C++ codebase for faster performance and optimize system designs by choosing suitable algorithms and architecture models.
The exact cost of a LiDAR app will depend on specific project details. The development cost of a LiDAR app can range from USD 10,000 to USD 75,000. More complex apps with AR and car projects can reach USD 1,25,000.
It’s essential to work with a reputable app development partner who can help you minimize costs and optimize your development process.
LiDAR app development services are extensively used in construction, manufacturing, utilities, agriculture, aviation, and the military. They continue to be a major contributor to accurate surveying, planning, quality control, and safety enhancements in these industries.
LiDAR has emerged as a favorite among many industries, including logistics, mining, aerospace, construction, manufacturing, robotics, energy, automotive, agriculture, and even military. Many global startups have also developed their own LiDAR software products:
Outsight – a French startup developing LiDAR-based robots for situational awareness.
Mach9 – A US-based startup developing software products for generating 3D engineering models for construction images.
Emesent – An Australian startup that develops hovermaps by combining drones and LiDAR sensors.
The technology is also efficiently utilized in aerospace and defense vehicles, from autonomous underwater vehicles (AUVs) to solid-state LiDAR sensors.
Miniaturized LiDARs: Miniaturizing LiDAR improves efficiency, cost, and obstacle avoidance in drones. This enables drones to map vast and inaccessible terrains more effectively.
Solid-state LiDARs: Conventional LiDAR uses mechanical components for scanning, while solid-state LiDAR is static. This reduces costs and maintenance and improves system reliability.
AI-driven LiDARs: AI-enhanced LiDAR can recognize objects, make judgments for autonomous vehicles, and optimize traffic management by analyzing traffic flow and pedestrian patterns.
At Moon Technolabs, we are at the forefront of LiDAR app development. Our team of experts has the experience and knowledge to help you develop your preferred type of LiDAR sensor technology for your business.
Whether you need a custom LiDAR app or want to integrate LiDAR into an existing application, we provide efficient data labeling, AI, and advanced automation. Contact us today to discuss your LiDAR app development project, and let us help you achieve your goals.
Create accurate 3D models for your business and streamline operations for your users.
While it’s still relatively new, LiDAR technology has the potential to revolutionize a wide range of industries and applications. From autonomous vehicles to augmented reality, LiDAR offers unique capabilities that can enhance user experiences and drive innovation.
Apple, Snapchat, and TikTok have already joined the AR and LiDAR bandwagon with their Pro models. It’s only a matter of time before AR and LiDAR take over the rest of the world.
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Jayanti Katariya is the CEO of Moon Technolabs, a fast-growing IT solutions provider, with 18+ years of experience in the industry. Passionate about developing creative apps from a young age, he pursued an engineering degree to further this interest. Under his leadership, Moon Technolabs has helped numerous brands establish their online presence and he has also launched an invoicing software that assists businesses to streamline their financial operations.
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