Multisensing

Multisensing refers to the utilization of multiple sensors to gather information about an environment or object. These sensors can be of various types, such as optical, thermal, acoustic, chemical, or physical, and are often integrated into a single system or platform. The primary goal of multisensing is to enhance the overall understanding and accuracy of the data collected by leveraging the complementary strengths of different sensing modalities. By combining data from multiple sources, systems can achieve a more comprehensive and robust perception than would be possible with a single sensor alone.

This approach is particularly useful in applications where a single sensor might be limited by factors like illumination, weather conditions, or the inherent properties of the target. For instance, in autonomous driving, a vehicle might use cameras for visual perception, lidar for precise distance measurement, and radar for detecting objects in adverse weather. The fusion of data from these sensors allows the vehicle to build a more reliable model of its surroundings, improving safety and navigation. Similarly, in environmental monitoring, combining data from air quality sensors, temperature sensors, and satellite imagery can provide a more complete picture of pollution levels and their spread.

The process of multisensing typically involves data acquisition, where each sensor collects its respective data, followed by data fusion. Data fusion techniques aim to combine the information from different sensors in a meaningful way, often involving calibration, alignment, and intelligent algorithms to resolve discrepancies and extract higher-level insights. The outcome is an improved, more accurate, and often more resilient understanding of the system's environment.