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LiDAR annotation

As we covered image and video annotation, next comes LiDAR, a fancy abbreviation of light detection and ranging. Lidar is a remote sensing technology that uses laser pulses to measure distances between objects. LiDAR annotation has changed the game of data annotation and we’re going to show you how.

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What is LiDAR data and why annotate it?

The annotation techniques that we already discussed mostly covered detecting data in 2D space. However, let’s not overlook the fact that we need a tool to calculate 3D information such as depth, the distance between objects, the reflectivity of the objects, and other cases where 2D techniques lack efficiency. LiDAR annotation addresses such issues and we’re about to find out how.

To understand the most common application of LiDAR data annotation, let’s first learn a new terminology: sensor fusion. Sensor fusion is the process of data collection from multiple sensors to create a more accurate and comprehensive understanding of the environment. In fact, information from just one source tends to be more biased and incomplete compared to combined annotated data from different sources. LiDAR is great for detecting the distance and position of objects in 3D space, but it can’t always provide the full picture. That’s where images come in, providing additional details such as color and texture.

LiDAR gained its popularity mainly after the recent hype around autonomous vehicles. As self-driving cars become more and more prevalent, LiDAR annotation emerges as a key technology that enables them to safely navigate their surrounding. Let’s discuss the case of fusing LiDAR and images to create a more robust and accurate perception of autonomous vehicles.

LiDAR annotated data can provide accurate distance measurements for detecting obstacles and identifying road features. However, LiDAR data annotators alone cannot provide detailed information about the color, texture, and appearance of objects. By combining annotated images with LiDAR data, autonomous vehicles can extract additional information, such as object color and texture to facilitate their understanding of the environment.

LiDAR annotation use cases

LiDAR segmentation: Existing for the past 10 years, LiDAR technology has very recently become a hot topic, especially in LiDAR autonomous driving, due to its ability to deliver detailed 3D information about a vehicle’s surroundings. This information includes obstacles, their position, the velocity with respect to the vehicle, and other data which is crucial for a safe driving experience. LiDAR segmentation tries to predict a point and labels it based on predefined labels. Accurate LiDAR segmentation algorithms allow autonomous systems to correctly identify all the obstacles and the road in the street thus making driving safer.

Object detection: Object detection with 3D bounding boxes for LiDAR data is the process of identifying and classifying objects in the point cloud data. Object detection in LiDAR data can be done much easier than segmentation and is often used for detecting pedestrians and cars for autonomous driving companies.

Sometimes the LiDAR data is collected as a sequence of frames. In such cases, Object Tracking becomes an important part of LiDAR annotation. Interpolation, AI-assisted labeling, and automated tracking algorithms are becoming essential in cases when one wants to perform fast and accurate annotation on LiDAR data.


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