In the pursuit of achieving Level 4 and 5 autonomous driving, the integration of various sensors has become crucial. According to a report by NXP, achieving L4/5 autonomous driving may require integrating up to 8 radars, 8 cameras, 3 LiDARs, and other sensors. Each sensor possesses its strengths and weaknesses, making it clear that no single sensor can fulfill all the requirements for autonomous driving.
Autonomous vehicles must employ a fusion of multiple sensor systems to ensure a dependable and safe driving experience. Integrating sensor data is vital in developing resilient self-driving technology to navigate diverse driving scenarios and adapt to varying environmental conditions.
Sensor fusion enables the amplification of the unique strengths of each sensor. For instance, LiDAR is exceptional at delivering depth data and recognizing the three-dimensional structure of objects. On the other hand, cameras play a crucial role in identifying visual characteristics like the color of a traffic signal or a temporary road sign, especially over long distances. Meanwhile, radar proves highly efficient in adverse weather conditions and when moving objects need tracking, such as an animal unexpectedly running onto the road.
This blog focuses on the diverse labeling requirements for effective sensor fusion to advance autonomous mobility.
Labeling Types for Multi-Sensor Fusion
3D Bounding Box/Cuboid:
3D bounding box annotation accounts for a depth /height of an image apart from length and breadth. It provides information about the object’s position, size, and orientation, which is crucial for object detection and localization.
3D Object Tracking:
3D object tracking involves assigning unique identifiers to objects across multiple frames in a sequence. It requires labeling the objects’ positions and trajectories over time, enabling applications such as autonomous driving and augmented reality.
2D-3D Linking:
2D-3D linking involves establishing correspondence between objects in 2D images and their corresponding 3D representations. It requires annotating both the 2D image and the corresponding 3D point cloud, enabling tasks such as visualizing the 3D structure of objects from 2D images.
Point Cloud Semantic Segmentation:
Point Cloud semantic segmentation involves assigning semantic labels to individual points in a 3D point cloud. This labeling technique enables understanding and categorizing different parts of objects or scenes in 3D, facilitating applications such as autonomous navigation and scene understanding.
Object Classification:
Object classification involves labeling objects in a 3D scene with specific class labels. It focuses on categorizing them into predefined classes, providing information about the types of objects present in the scenario.
3D Polyline:
3D polyline labeling entails annotating continuous lines or curves in a 3D space. It is apt for road or lane marking, where precise delineation of boundaries or paths is required.
3D Instance Segmentation:
3D instance segmentation involves labeling individual instances of objects in a 3D scene with unique identifiers. It provides detailed information about the object boundaries and allows for distinguishing between multiple instances of the same object class.
Each of these diverse labeling requirements plays a vital role in sensor fusion, where data from multiple sensors, such as cameras and LiDAR, are integrated to create a comprehensive understanding of the environment in 3D. These labels enable robust perception systems for various applications, including autonomous driving, robotics, and augmented reality.
Benefits of Outsourcing Sensor-fusion Data Labeling
Enhanced Accuracy and Quality:
Data labeling companies have dedicated teams of trained annotators who specialize in sensor fusion tasks, ensuring accurate labeling and reducing errors that may arise from in-house labeling.
Scalability:
As sensor data increases in complexity and volume, outsourcing ensures that a data labeling partner can quickly scale up their resources and meet the demands without putting strain on internal teams, resulting in faster turnaround times.
Flexibility:
Data labeling partners that offer custom labeling workflows provide a tailored approach that aligns with the specific needs and requirements of the sensor fusion project. This benefit ensures that the labeling process is optimized for the unique characteristics and complexities of the data, leading to more accurate and precise annotations.
Domain Expertise:
Data labeling partners that include a workforce with domain expertise in sensor fusion tasks understand the nuances of labeling different sensor modalities, such as LiDAR, radar, and cameras, and can effectively handle various sensor fusion use cases. Leveraging their expertise can lead to more accurate and reliable labeled data for training sensor fusion algorithms.
Cost-effectiveness:
Outsourcing data labeling needs for sensor fusion can be cost-effective compared to building an in-house team. Setting up an internal data labeling infrastructure, including hiring and training annotators, acquiring labeling tools, and managing the process, can be expensive. Outsourcing allows organizations to focus on their core competencies while benefiting from the cost savings of leveraging external expertise.
Time Savings:
Data labeling is a time-consuming process that requires significant effort and attention to detail. By outsourcing this task, organizations can save valuable time and allocate resources to other critical aspects of their projects.
Conclusion
Highly-accurate labeling of data collected from multiple sensors of an autonomous vehicle is crucial to improve the performance of computer vision models. At iMerit, we excel at multi-sensor annotation for the camera, LiDAR, radar, and audio data for enhanced scene perception, localization, mapping, and trajectory optimization. Our teams use 3D data points with additional RGB or intensity values to analyze imagery within the frame to ensure that annotations have the highest ground-truth accuracy.
