The research team addressed this problem by implementing a system capable of adaptive control of the data volume sent by each sensor. In their design, each LIDAR unit is connected to a sensor control device, which grabs, buffers, and transmits the data to an edge computer. The computer constantly monitors and aggregates the data streams from each sensor, carefully inspecting their latency and jitter.

Most importantly, if the computer detects large delays from a given sensor (which can happen due to network or bandwidth problems), the sensor is instructed to adjust the data volume it transmits. For this purpose, each sensor carries a filter that can increase or reduce the size of the point cloud transmitted based on the “importance” of each region in 3D space, which is predetermined by the administrator for each specific use case. By discarding the least important points from sensors experiencing bandwidth limitations, the quality of the aggregated point cloud can be preserved as much as possible.

The researchers tested their system in two experimental indoor environments under a variety of conditions. “The proposed design was evaluated with various load patterns from 100 to 200 MB/s, including dynamic loads that differed in size minute to minute,” explains Akiyama.

“We found that our system was able to satisfy delay requirements and receive highly important points even if the network was under dynamic load. This suggests that our approach is effective when an object moves within the monitoring area and when the available network bandwidth varies, regardless of the physical speed of wireless communication.”

One of the main use cases for multi-LIDAR sensor networks, both indoor and outdoor ones, is creating virtual replicas of the real world, called digital twins.

“A sensor network with a large number of LIDAR sensors can be leveraged to construct a digital twin that covers a wide area. Such extensive digital twins could be continuously updated in real time to capture the movements of people, vehicles, and mobile robots to optimize autonomous driving systems,” highlights Akiyama.

“These optimizations could improve safety and efficiency of autonomous mobility while saving reducing costs.”

Further efforts in this field will help accelerate the advent of smart cities and all their benefits, making urban spaces better places to live.