Humans effortlessly perceive the world through their senses. But...it's quite a challenge for machines. To endow machines with sensory power, a variety of sensors have emerged, such as smoke detectors and infrared sensors on automatic doors.

Unity is strength: From Mono Sensor to Sensor Fusion

Into the World of Sensors...

In the realm of autonomous driving, a vehicle's data perception system consists of multiple sensors, such as cameras, Lidar, and Radar, to detect and track objects in the vehicle's surroundings, including other vehicles, pedestrians, and obstacles. By combining and analyzing the data collected from these sensors, the perception system provides detailed information about the environment around the car, which is essential for ensuring safe and efficient navigation. Like the eyes and ears of an autonomous vehicle, the perception system enables the car to make informed decisions about its actions on the road, such as maintaining a safe distance from other cars, avoiding obstacles, and responding to traffic signals, ultimately contributing to the overall safety and performance of self-driving cars.

An NXP report shows that achieving L4/5 autonomous driving may require integrating up to 8 radars, 8 cameras, 3 LiDARs, and other sensors. Cameras, LiDAR, and (millimeter-wave) radars each have their pros and cons:

1. Range and Spatial Resolution: LiDAR systems excel in providing high-resolution 3D scans up to 200 meters away, while cameras offer detailed object recognition at closer ranges. Radar performs well for detecting objects at varying distances but lacks spatial resolution.

2. Robustness in Darkness and Weather: LiDAR and radar are active sensors, making them highly reliable in darkness and challenging weather conditions. Cameras, being passive sensors, are less effective in low-light scenarios.

3. Object Classification and 2D Perception: Cameras are unmatched in their ability to classify objects and interpret 2D structures like traffic signs and lane markings. LiDAR can provide some classification through 3D point clouds, but radar is limited in this regard.

4. Speed Measurement and Computational Requirements: Radar directly measures object speed with high accuracy, whereas LiDAR and cameras use indirect methods. Camera-based systems require significant computational resources for real-time image processing, whereas LiDAR and radar are less demanding.

5. Cost, Package Size, and Integration: Cameras and radar sensors are affordable and can be easily integrated into vehicles. LiDAR costs are decreasing and solid-state LiDAR systems are becoming smaller, making them more viable for automotive integration in the future.

Each sensor type has its own strengths and weaknesses, and no single sensor is sufficient for autonomous driving. To achieve reliable, safe, and accurate operation, autonomous vehicles must integrate multiple sensor systems to create a comprehensive, redundant, and complementary data set. This fusion of sensor data is a critical aspect of developing robust self-driving vehicles capable of handling diverse driving scenarios and environmental conditions.

Camera-LiDAR 2D & 3D Sensor Fusion

Camera-LiDAR 2D & 3D sensor fusion is considered advantageous over other sensor combinations, such as camera-radar or radar-LiDAR fusion, for several reasons:

1. Complementary Data: Camera-LiDAR fusion combines the rich 2D visual information from cameras with the precise 3D point cloud data provided by LiDAR. This combination results in more comprehensive environmental perception, as cameras excel in object recognition, classification, and 2D structure interpretation, while LiDAR contributes detailed depth and spatial awareness. In contrast, radar primarily offers distance and velocity information but lacks the resolution and object classification capabilities of cameras and LiDAR.

2. Higher Resolution: LiDAR provides significantly higher spatial resolution than radar, making it better suited for detecting small or complex objects and shapes in the environment. This advantage is particularly valuable when combined with camera data, as it enables more accurate object identification and tracking.

3. Robustness: While both LiDAR and radar perform well in challenging weather and lighting conditions, LiDAR's higher resolution makes it a better complement to cameras, which may struggle in low-light or adverse weather scenarios. The fusion of camera and LiDAR data can help mitigate the limitations of each individual sensor, resulting in a more robust perception system.

4. More Accurate Object Classification: Camera-LiDAR fusion can improve object classification and identification by leveraging the strengths of both sensors. Cameras offer excellent object recognition and classification based on color and texture, while LiDAR's 3D point cloud data can provide additional information about an object's shape and size. This combined information enables more accurate and reliable decision-making in autonomous systems, compared to the fusion of camera-radar or radar-LiDAR data.

How to Annotate 2D & 3D Sensor Fusion Data

Developed by the BasicAI team, BasicAI Cloud* is an AI-powered multimodal data annotation platform. One of the highlights of BasicAI Cloud* is its strong support of annotating 2D & 3D sensor fusion data (two key concepts you need to know about sensor fusion annotation: intrinsic and extrinsic). With this powerhouse, you can:

1. Speed through auto-annotation 82x faster than manual methods in labeling sensor fusion data

2. Track objects in sensor fusion frame series like a pro, thanks to optimized object tracking models

3. Create a variety of annotations (3D cuboids, 2D cuboids, 2D bounding boxes, polygons, polylines, and curves) using auto tools, semi-auto tools, and manual tools

4. Project point cloud annotations onto related images (in 2D cuboids or bounding boxes) with ease

5. Easily review and adjust your annotations (extremely easy for objects in frame series) from different views and data types

6. Tackle larger datasets – up to 150 million points with 300 consecutive frames supported

7. Auto-segment point cloud data and create segmentations in related images with the help of point projection and filters

8. Collaborate efficiently with your team using streamlined annotation teamwork management

9. And so much more!

Let's Get Started! Prepping Your 2D & 3D Sensor Fusion Data

Ready to dive into annotating 2D & 3D sensor fusion data on BasicAI Cloud*? First, re-arrange your dataset like this:

Make sure all data is placed in different folders with matching file names. The 2D & 3D LiDAR Fusion dataset sample can be downloaded from here. This dataset is provided by PandaSet.

Annotating Sensor Fusion Data on BasicAI Cloud*

The user-friendly interface of BasicAI Cloud* consists of several parts, including tools for creating and adjusting annotations, frame management, settings, cuboid views, and results display. And here's the cherry on top: you can even run a pre-trained model that predicts annotation results with remarkable accuracy!

Tools

• 3D Cuboid Creation: Choose between manual (three clicks around targets) and AI-assisted (click & drag) methods to create flawless 3D cuboids.

• Annotation Projection: Project annotations onto related 2D images in a snap.

• Pre-trained Model Integration: Leverage the power of AI to predict annotation results automagically.

[Discover BasicAI Cloud*'s game-changing auto-annotation tools](See how BasicAI Cloud* auto-annotation tools can change the game ->)

Frame Management for Frame Series

• Frame Navigation: Easily switch between different frames in your dataset with consecutive frames.

• Auto Load: Turn on auto-loading to automatically fetch the data you need.

Settings

• Customization: Tweak settings to better identify objects, like displaying point intensity values and changing point sizes.

Cuboid Views

• Fine-tuning: Refine your 3D point cloud data cuboids to achieve the best possible fit for your target objects.

Results

• Label Display: Check out the annotated object results and filter labels to find what you're looking for.

2D & 3D Sensor Fusion is Gaining the Stage

With advances in computational power, battery life, and decreasing chip prices, higher-resolution cameras and 3D sensors are becoming more prevalent. As a result, the fusion of 2D and 3D data is becoming increasingly important for various applications.

Sensor Fusion in Action

1. Autonomous Vehicles: Sensor fusion is crucial for self-driving cars to safely navigate their surroundings. Combining 2D camera images and 3D LiDAR point cloud data provides richer information for object detection, tracking, and classification.

2. Robotics: Fusing 2D and 3D data helps robots better understand their environment, improving object recognition, manipulation, and navigation, ultimately boosting performance and efficiency.

3. Smart Cities: Sensor fusion can benefit urban planning, infrastructure management, and public safety in smart cities. Detailed 3D maps created from 2D images and 3D point cloud data can aid in planning, monitoring, and maintenance.

4. AR and VR: Augmented and virtual reality experiences can be enhanced through sensor fusion. Combining 2D images and 3D point cloud data creates more realistic and immersive 3D models of real-world environments.

5. Remote Sensing and Geospatial Analysis: Sensor fusion allows for more accurate 3D representations of terrain and landscapes, making it invaluable for environmental monitoring, natural resource management, and disaster response.

With BasicAI Cloud* in your arsenal, you're well-equipped to tackle the challenges and opportunities presented by the growing importance of 2D and 3D sensor fusion. Enjoy annotating!

* To further enhance data security, we discontinue the Cloud version of our data annotation platform since 31st October 2024. Please contact us for a customized private deployment plan that meets your data annotation goals while prioritizing data security.