We have been using LiDAR for decades, but its true potential has been realized by AI now. With the introduction of Light detecting and ranging technology, AI has come with multiple techniques. Ai has witnessed unleashing of the potential of light-detecting technology. Let us get to know more about the collaboration oF AI with LiDAR. Before that, we must know more about technology.
LiDAR is a remote sensing technology that detects light and its range. In the early 60s, LiDR was used on planes to detect terrains. But, with the introduction of the Global Positioning System (GPS), we can see LiDAR applications growing day by day. GPS enables the data collected from the scans of LiDAR to build 3D models. LiDAR's applications cost so less, and the data availability is ample. LiDAR's perfect blend of Machine Learning and Artificial Intelligence has opened many doors of innovation.
The LiDAR Data is a sensor for geospatial technology, and it has many industrial applications. It uses lasers, scanners, and GPS receivers to calculate the distances. LiDAR Data Annotation is a challenging and time-consuming task as it demands an expert-level understanding
LiDAR instruments are used in Airplanes and Helicopters to acquire LiDAR data over broad areas. LiDAR systems allow scientists to examine both natural and artificial environments. Analyzing the environment is quite accurate, precise, and flexible. NOAA scientists use LiDAR in producing more accurate shoreline maps. They also use it for making digital elevation models for geographic information systems
The LiDAR system works with four key elements: Scanner, sensor, laser, and GPS.
Scanner-: A scanner is used to regulate the speed of the scans of the targetted objects. The scan also includes the distance that the laser can cover.
Sensor-: A sensor must measure the length of the time taken for the light to reflect from the target object and strikes the LiDAR system.
Laser-: An LiDAR laser sends the light pulses to target objects. These light waves can be UV, visible, or infrared.
GPS-: The last element is GPS. It simply tracks the system location to check the measurements between the system and the target object.
A modern LiDAR system is privileged with sending 5 lakh pulses every second. The system integrates and aggregates these pulses into a cloud with numerous points. This cloud point becomes a dataset of coordinates that represent all the objects.
The relationship between these two is quite simple. The duty of LiDAR is to collect numerous 3D points to create a point cloud. After this, AI focuses on processing the data. An average LiDAR system pulse rates vary from 10,000 to 2Lakhs pulse per second. These can generate multiple returns from the same pulse. The return generated from LiDAR can get processed with the help of AI models to sense a given environment.
Earlier, it was hard for the teams to label the generated data by LiDAR. This data got manually labeled to identify key objects present in the scan. The team's efforts were going to waste as the task was time-consuming. Computer Vision had given life to those workers spending day and night labeling up the data collected. With the help of Artificial Intelligence, the process is automatic. AI can even process unstructured input and accurately provides output. It is through the work of LiDAR and AI that we can develop 3D models of our world and space.
As we got to know about LiDAR, we analyzed that there are several real-world applications of it. It has become relevant to architecture, manufacturing, printing, virtual reality, and oceanography. Let us go deep into the ocean:
Self-driving cars-: Self-driving cars are not that popular because of the high cost and maintenance, but there will going to be a day with maximum use. These autonomous cars require AI-powered LiDAR systems to scan the area nearby them. LiDAR systems create 3D models so that the vehicle makes instantaneous decisions. Accurate LiDAR systems are required to ensure the safety of the passengers.
Agriculture-: These systems are installed on drones for quick capture of topographical features of the area. With the help of drones, the farmers create a map of the elevated land. It helps farmers to recognize the ideal area to grow crops. It also helps them to identify the areas requiring fertilizers and pesticides.
Defense-: LiDAR systems help in scouting borders and identifying suspicious activities. With AI, militaries can see autonomous surveying of border environments. The LiDAR system helps them get aware of the potential danger.
Text Annotation-: This is the most commonly used data type. Our team focuses on its wide range of annotations like sentiment, intent, and query.
Semantic Annotation-: We perform Semantic Annotation that improves product listing. By this approach, we ensure your customers find all their products. We tag the various components within the product titles and descriptions.
Named Entity Annotation-: NER systems require a large amount of manually annotated data. Our annotators label up your data so that you can build an effective NER system. We apply all our annotation capabilities to help our clients.
Audio Annotation-: This annotation is the transcription of speech data that includes specific pronunciations. Along with the pronunciation, audio annotation includes identification of dialect, language, accent, and demographics.
There are several other kinds of annotation like Image And Video Annotationthat are relevant to LiDAR Annotation. GTS’ annotators are going to help you with each kind of annotation that you demand.
Multiple applications of LiDAR make it more of use. LiDAR and Cloud Annotation work best with high-quality datasets. AI Data Annotation can pose some challenges unless done right.
GTS helps with high-quality data collection for your LiDAR systems. Our team focuses on Cuboid Annotation, Sensor Fusions, Point Cloud Focus, and Topographic Depth View. We can help you additionally with labeling up your data to build datasets suitable for LiDAR annotation. Give us a try and enjoy it forever!