Applications & Challenges with 3D Point Cloud Data for LIDARs

3D point cloud data is one of the most complex types of machine learning data used to generate the most useful training datasets for autonomous vehicles. LiDAR data is annotated with 3D point cloud annotation to make the objects more precisely detectable to a self-driving car.

LiDAR & 3D Point Cloud Annotation

A bunch of LIDAR data put into the deep learning algorithms, helps autonomous cars detect and measure the distance of objects precisely. Dodges in the making the LIDAR datasets can give inaccurate information to the car resulting in wrong interpretation increase the chances of collisions.

Also Read: LIDAR Sensor in Autonomous Vehicles: Why it is Important for Self-Driving Cars

3D point cloud annotation is the most useful as well as challenging image annotation technique used to generate data that helps deep learning algorithms to better understand the surroundings. The LIDAR data can be made useful for autonomous vehicles only when it is annotated with 3D point cloud data.

3D Point Cloud Data

Point clouds can easily describe objects measuring from just a few millimeters to objects as large as buildings, trees, and even entire conurbations.

And to collect geographic point cloud data LiDAR sensing scanners are used that can gather the accurate mapping of the objects. LiDAR can shoot pulses of light up to a million pulses per second that bounce off a surface and return to the sensor that calculates the time it took for the ray of light to return and which direction it came from.

The entire process ultimately creates a point cloud map of the scanned surroundings to store every detail down to the last millimeter, while capturing all the different contours and shapes. But there are few shortcomings with LiDAR and using the same for gathering such data.

Limitations of LiDAR Data

LIDAR Limitations:

  • LiDAR needs visible to access real objects.
  • Reflective surfaces can create problems for the laser.
  • It’s not suitable for capturing moving objects.
  • Bad weather can interrupt data collection.
  • Investment in LiDAR is very costly for researchers.

Though, LiDAR has its limitations but still it is one of the best sensing technologies providing more useful information to autonomous cars. The LiDAR data becomes more useful when it is labeled with 3D cloud annotation.

Applications of 3D Point Data

Self-driving Cars or Autonomous Vehicles

Also Read: Why Self-driving Cars Taking Too Much Time: Challenges of Autonomous Vehicles

In Other LiDAR-based Applications

If your AI model is going to be used in the real-world where the accuracy of gathering such data is practically associated with life and death situation, then you need to rely on LiDAR for capturing the tiny details.

To Explore Dense Forest & Dangerous Hidden Locations

This hidden ancient place has amazed the archeologists, especially about the potential of LiDAR technology. Yes, LiDAR can be used in various other dangerous situations the places that are not safe for human mapping manually.

But robots or drones with LiDAR sensors can reach such dangerous locations and scan or generate useful data helping humans to navigate safely at such risky places and avoid mishaps. Natural disasters, unknown hidden dense forests and dark caves are the best examples of such dangerous locations.

Challenges with 3D Point Cloud Data

Massive Size of Files

Complex User Interface

Highly Costly Sensors

Although, more affordable options are available for researchers expansive sensors can provide the details that low-cost devices can’t. But if you want to train your deep learning model with a better quality of data, you need to go with the superior quality of sensors and training data.

Also Read: Top Data Labeling Challenges Faced by the Data Annotation Companies

Better quality data here means the computer vision training data you need to train your machine learning or deep learning model. Anolytics right here doing the same job with a team of dedicated and highly skilled annotators for annotating all types of data including LiDAR.

For 3D point cloud annotation, our enriched experience and expertise in image annotation services will give an advantage of getting high-quality training data for self-driving cars, robots, and drones. It offers a human-powered AI-assisted data labeling service to produce world-class training datasets.

Also Read: Why Human Annotated Datasets is Important for Machine Learning

Originally published at- Anolytics

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