multisensordata

Multisensordata refers to data collected from multiple sensors, often of different modalities, in order to capture complementary information about a scene, object, or process. The term is widely used in fields such as robotics, autonomous systems, and environmental monitoring.

The main motivation is to improve accuracy, robustness, and situational awareness by combining signals such as visual, depth, thermal, acoustic, and chemical sensors. By fusion, systems can compensate for the limitations of any single sensor and operate across a wider range of conditions.

Data fusion approaches vary. Early fusion combines raw or extracted features at the input level, while late fusion integrates independent sensor decisions; intermediate fusion blends representations at intermediate stages. Techniques include probabilistic fusion (Bayesian methods), Kalman and particle filters, and deep learning-based fusion networks.

Key requirements include temporal and spatial alignment, sensor calibration, and data normalization. Challenges include heterogeneous data formats, different sampling rates and noise characteristics, missing data, computational demands, and privacy and security concerns.

Applications span autonomous driving and robotics, smart cities, surveillance, healthcare and wearables, and environmental monitoring and disaster response.

Common benchmarks and datasets used for multisensor fusion include KITTI, NuScenes, and Waymo Open Dataset, as well as RGB-D and infrared-visual fusion datasets. Standards emphasize calibration, time synchronization, and interoperable data formats.