What strategies can you use to deal with LiDAR occlusions?

Before messing with the LiDAR we should deploy it where is intended to be. Analyze the environment according to the operating and technical manuals and then start using the recommendation listed in this article

Integrate LiDAR with other complementary sensors, such as cameras or radar, to compensate for occluded areas. By combining data from multiple sensors, you can obtain a more comprehensive and robust understanding of the environment.

By strategically deploying multiple LiDARs, applying robust sensor fusion techniques, and optimizing for real-time performance, you can significantly enhance your robot's ability to perceive the environment accurately and navigate effectively even in the presence of occlusions … specific implementation will depend on your robot's capabilities, operational environment, and desired level of safety and accuracy. Few tricks - 1) place them at different heights and angles to provide a more complete view of the environment ; 2) use LiDARs with different wavelengths, which can penetrate different materials and reduce the impact of occlusions. Please note that using multiple LiDARs will increase the complexity & cost so trade-off when designing

To mitigate LiDAR occlusions, employing multiple LiDARs strategically positioned and oriented differently is an effective strategy. For instance, placing one LiDAR atop the robot and another at the front or side enhances data coverage and diversity, compensating for blind spots by capturing data from various angles. However, this approach does raise the system's cost, complexity, and power demands, and necessitates meticulous calibration and synchronization. The key is balancing these drawbacks against the enhanced spatial awareness multiple LiDARs provide, ensuring a more comprehensive and reliable sensory input for navigation and obstacle detection.

Sensor Redundancy with Different Wavelengths: Combine LiDAR sensors operating at different wavelengths (e.g., near-infrared and mid-infrared) to improve data capture in challenging lighting conditions.

Multiple lidars can help to improve the perception of the environment by covering a larger field of view and reducing blind spots. However, calibrating multiple lidars can be challenging, especially when they have different resolutions or negligible overlap. There are different methods to calibrate multiple lidars, such as using markers, trajectories, or optimization algorithms

Combine LiDAR data with other sensor modalities like cameras or radar. Cameras can "see" around occlusions, while radar can penetrate certain materials that LiDAR can't. By fusing data from different sensors, you can get a more complete picture of the environment and compensate for missing information due to occlusions.

Accurately modeling the uncertainties associated with occlusions requires careful calibration of sensors and understanding the limitations of data acquisition methods. Balancing exploration and exploitation is crucial in probabilistic methods. Robots need to gather enough information to reduce uncertainty while acting efficiently and achieving their goals. Computational cost can be a factor, especially for complex environments with numerous occlusions. Choosing efficient algorithms and hardware is essential for real-time performance.

Temporal integration, i.e. aggregating several frames, is an easy way of dealing with temporary occlusions : if there was an obstacle at some position a few seconds ago, but now the obstacle is occluded (say by a moving vehicle), chances are the obstacle still is at the same position. This also work for self occlusion if you are able to determine your ego-motion : if the robot saw an obstacle there a few seconds ago but moved an can't see the obstacle anymore (because the robot's arm if blocking the view), chances are that the obstacle is still there.

- Deep learning models trained on diverse datasets of LiDAR point clouds and camera images can recognize object shapes and predict their trajectories even with limited visibility. - Generative AI models can be used to create synthetic environments with various occlusion scenarios ... allowing robots to train and test their occlusion-handling strategies in a simulated setting before real-world deployment. - Probabilistic models help robots adapt their behavior in real-time and handle unexpected situations caused by occlusions more effectively. - ML can be employed to detect anomalies in point cloud data that might indicate hidden objects or potential risks due to occlusions.

If you have a motion model of you semantic object, you can infer its position during the occlusion. Example : you are able the track a pedestrian and therefore know its speed and direction, so you are can infer the position of the pedestrian even when it walk behind a car.

Dynamic Sensor Mounting: Employ adjustable or dynamic mounting systems for LiDAR sensors. This allows for real-time adjustments to the sensor’s position to mitigate occlusion.

Consider using multiple LiDAR sensors with different scanning patterns or viewpoints to minimize occluded areas. Optimizing the placement of LiDAR sensors on a vehicle or robot can also improve visibility and reduce occlusions.

To tackle LiDAR occlusion, a multi-sensor integration strategy is key. Combining LiDAR with other sensors like cameras and radar can offset occlusion issues by leveraging their complementary strengths. Cameras provide detailed visuals, while radar excels in poor visibility conditions. Employing advanced data fusion algorithms is crucial for merging diverse sensor data, leading to a more complete environmental representation. Machine learning can also be used to predict and compensate for occluded areas. Regular sensor calibration ensures accuracy. This approach enhances spatial awareness and mapping accuracy in projects where understanding the full environment is essential.

No single strategy is a magic bullet. The most effective approach for dealing with LiDAR occlusions depends on the specific application, sensor configuration, and environmental conditions. Combining these strategies and adapting them to your specific needs will help you build robust robotic systems that can handle occlusions effectively and operate safely in real-world environments.

In addressing LiDAR occlusions, consider data fusion techniques for effectively integrating information from various sources. Be mindful of environmental factors like weather and lighting, as they significantly impact LiDAR performance. Real-time processing is crucial, especially in applications requiring immediate responses, such as autonomous vehicles. This necessitates a balance between computational efficiency and algorithm sophistication. Additionally, factor in the cost and complexity; more sensors and advanced methods increase both. The aim is to develop a LiDAR system that's adaptable, efficient, and practical, striking a balance between enhanced occlusion handling and maintaining affordability and simplicity.