How AI Systems Rely on LiDAR to Navigate Their Surrounding
While LiDAR is often associated with autonomous vehicles, AI robotic systems, in general, rely on LiDAR to navigate their surroundings. For example, AI-powered robots are often used in the agriculture area to perform mundane tasks like picking ripe fruits and vegetables, removing weeds and other activities. As we shall see later on, Mindy Support has worked on a LiDAR project to identify noise walls and signs on railroads. Before we get into the use cases, let’s first get an understanding of what LiDAR actually is.
What is LiDAR?
LiDAR is an acronym for “light detection and ranging”. LiDAR is similar to traditional radar since it sends out pulses of electromagnetic radiation to detect objects in its surroundings. These lasers bounce off the objects and return back to the car which allows the system to measure the distance. LiDAR offers ultra-fast response time to allow the AI system to make quick decisions to react to changing circumstances on the road. The biggest advantage of LiDAR is the accuracy it offers.
LiDAR images produce a 3D Point Cloud which is a representation of how the computer sees the physical world. This also needs to be annotated to label all of the items in the image and their distance from the vehicle. This is usually done with colors representing short and long wavelengths. For example,when working with 3D Point Clouds for the automotive sector, the road on the 3D Point Cloud will always be blue since this color has a short wavelength and the road itself is the lowest point on the image.
LiDAR for Automotive
The days of autonomous cars on the road seem to get closer every year with new inventions and developments announced to make the car function correctly on the road. Most companies are using LiDAR along with cameras and radar to train the machine learning algorithms. We already mentioned that a 3D Point Cloud is essential to autonomous vehicles, but the algorithms constantly need to be perfected for self-driving cars to become mainstream. In fact, we are already seeing companies like Mitsubishi, Nissan and other manufacturers implementing LiDAR in their 2021 and 2022 car models.
Now that we know what LiDAR is and how it works, let’s take a look at some use cases Mindy Support has worked in the LiDAR area.
Mindy Support’s Use Cases in LiDAR
The use cases below demonstrate some of the challenges involved in actualizing LiDAR project and having the right experience allowed us to overcome those challenges.
Detecting and marking traffic signs and traffic lights
Purpose: Detection and marking traffic signs and traffic lights with 3D Cuboids on the Lidar clouds and with Bounding boxes on 2D images.
1) Many objects per task, wide variety of signs to be labelled.
2) Some tasks were overloaded with objects to be annotated.
Solution: Additional Approve and Quality Control stages for maintaining the quality on the high level.
Team: 100 agents within 6 months
Multi-sensor linking: Identifying noise walls and signs on the railroad
Purpose: Make multi-sensor linking by Identifying noise walls and signs on the railroad.
1) There was no ready-made tooling solution that could meet all of the project requirements.
2) Connecting coordinates of objects in the point cloud images taken by the LiDAR with the real coordinates of these objects on the map.
Solution: Developed our own technical solution to provide the required result for the client within set time frame.
Team: 95 agents within 2 months
Detecting and marking traffic signs and traffic lights
Purpose: Make point cloud segmentation, inter-frame data association and the track-level annotation.
1)Many objects to detect and track on the sequence of frames, including small objects.
2) Wide variety of categories and labels to be used.
Solution: Implemented multi-level training to strengthen skills of the team on 2D-3D object detections with the direction settlement and object’s interpolation.
Team: 80 agents within 7 months
Detecting and marking vehicles on sequences
Purpose: Detection and marking of vehicles on sequences with 50-70 frames, both LiDAR and 2D frames. Objects were captured from different positions
Challenge: Tracking each object on each frame within the whole sequence.
Solution: Prepared an extensive guide and conducted a multi-level training process to prepare the team to work with sequences in the most efficient way maintaining the high quality.
Team: 250 agents within 8 months
Detecting and marking traffic lights
Purpose: Detection and marking traffic lights on the LiDAR sequences
Challenge: Detection of different traffic lights types and tracking each object on the whole sequence.
Solution: Adding extra examples on types of traffic lights and their distinctive features.
Team: 75 agents within 5 months
Verification of auto-generated polygons and determination of objects’ nature
Purpose: Verification of auto-generated polygons and determination of objects’ nature, using multi-camera views.
Challenge: Variety of small and occluded objects, which were difficult to determine.
Solution: Expanded the guide with more detailed explanations on how to determine objects correctly and how to work with sequences of frames in the most efficient way.
Team: 105 agents within 4 months
Trust Mindy Support With Your LiDAR Data Annotation Needs
As we have seen in the use cases above, Mindy Support has extensive experience in the areas of LiDAR and 3D Point Cloud Annotation. We are one of the largest BPO providers in Eastern Europe with more than 2,000 employees in six locations all over Ukraine. Regardless of the amount of data you need to be annotated, we can assemble a team that will get the job done within the specified time period. Our rigorous QA process ensures that all tasks are done correctly the first time around which allows us to scale your team without sacrificing the quality.
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April 13th, 2021Mindy News Blog