IMU Networks for Trajectory Reconstruction in Logistics Applications.

Area of Science:

• Robotics and Sensor Networks
• Logistics and Supply Chain Management
• Data Fusion and Estimation

Background:

• Accurate trajectory reconstruction is crucial for logistics, particularly for verifying goods handling.
• Existing methods often require expensive sensors or complex computational resources.
• Low-cost Inertial Measurement Units (IMUs) offer a potential solution for mass-market logistics applications.

Purpose of the Study:

• To develop and validate a consensus-based approach for trajectory reconstruction using networks of low-cost IMUs.
• To adapt existing multi-target following strategies for distributed sensor networks in logistics.
• To integrate covariance intersection data fusion for enhanced trajectory estimation.

Main Methods:

• Implementation of a consensus algorithm to synchronize trajectory data across a network of IMUs.
• Adaptation of a literature strategy from multi-target following problems for IMU network consensus.
• Application of covariance intersection for fusing data from multiple IMU nodes.

Main Results:

• Demonstrated successful consensus achievement in trajectory reconstruction among IMU network nodes.
• Validated the effectiveness of the adapted strategy for distributed sensor networks.
• Showcased improved trajectory accuracy through covariance intersection data fusion.

Conclusions:

• The proposed network-based IMU approach enables accurate trajectory reconstruction in logistics applications.
• The method is suitable for low-cost sensors and basic computational devices, addressing economic constraints.
• This work provides a foundation for enhanced goods handling verification and supply chain optimization.