How do robot vision sensors handle occlusions in cluttered environments?

Robot vision sensors handle occlusions in cluttered environments by classifying different types, like partial or full occlusions. Strategies include predictive modeling, multi-view fusion, and machine learning to infer object identities based on contextual cues. Active sensing methods, like sensor movement, improve views of occluded objects. Understanding occlusion types and employing appropriate strategies enable accurate perception in cluttered environments.

Sensor fusion can be an effective way to occlusion detection effectively. Using this approach the robot can better estimate object positions and shapes even when they are partially occluded. For example, integrating data from multiple sensors, such as cameras, LiDAR, and depth sensors, allows the robot to build a more comprehensive understanding of its surroundings.

Deep learning approaches have shown promise in tackling occlusions. Convolutional Neural Networks (CNNs) trained on large datasets with synthetic and real occlusion scenarios can learn to identify and compensate for occluded regions. Generative Adversarial Networks (GANs) can generate realistic inpainted versions of the occluded areas, improving the overall completeness of the image data. Combining these techniques with traditional methods offers a powerful toolbox for robots to handle occlusions and achieve robust performance in real-world environments.

One of the most effective ways to handle occlusions involves tracking detections using vision-based Transformer models. This can be achieved by extracting their embeddings to check similarity scores, while simultaneously monitoring their motion through methods like optical flow and the Kalman filter. By assigning appropriate weights to both components, we can accurately finalize detections and their associated tracks.

If objects are occluded, tracking systems can help. Deep Learning models can work at varying degrees for partial occlusion, but you'll have to have a tuned system to be able to handle an object being occluded up to a certain amount of time without considering the object dead. If you're tracking a person, and you want to keep track of when they've gone into a building, and when they come out, even if it's hours later, it's possible to reID based on a previously captured signature, likely based on the previously tracked image. You'll just have to write your tracking algorithm to handle that.