<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The objective of the green logistics distribution model is to minimise environmental pollution and energy usage by employing clean energy, optimising transport routes and enhancing transport efficiency. Nonetheless, current studies on green logistics distribution models and warehousing planning exhibit certain drawback, such as imprecise location accuracy and decreased distribution revenues. To overcome these challenges, this paper proposes a novel approach that combines inertial measurement unit (IMU) and binocular vision, leveraging multisource information positioning. Specifically, the method integrates data collection and preprocessing modules to gather crucial logistics distribution task information, encompassing IMU data, image data and vehicle data. The visual and inertial positioning module consists of two components: visual positioning based on the grey centre method and IMU positioning based on the integral essence. Finally, an adaptive Kalman filter is employed to merge the results of visual positioning and IMU positioning, thus producing the ultimate logistics vehicle positioning result. The proposed method effectively addresses existing challenges in the green logistics distribution model and warehouse planning. In particular, the experimental results demonstrate that the algorithm proposed in this study reduces the location error by 8%. Furthermore, logistics and distribution costs are reduced by 11 %, contributing to the promotion of sustainable and environmentally friendly logistics operations.</p>
</abstract>

The objective of the green logistics distribution model is to minimise environmental pollution and energy usage by employing clean energy, optimising transport routes and enhancing transport efficiency. Nonetheless, current studies on green logistics distribution models and warehousing planning exhibit certain drawback, such as imprecise location accuracy and decreased distribution revenues. To overcome these challenges, this paper proposes a novel approach that combines inertial measurement unit (IMU) and binocular vision, leveraging multisource information positioning. Specifically, the method integrates data collection and preprocessing modules to gather crucial logistics distribution task information, encompassing IMU data, image data and vehicle data. The visual and inertial positioning module consists of two components: visual positioning based on the grey centre method and IMU positioning based on the integral essence. Finally, an adaptive Kalman filter is employed to merge the results of visual positioning and IMU positioning, thus producing the ultimate logistics vehicle positioning result. The proposed method effectively addresses existing challenges in the green logistics distribution model and warehouse planning. In particular, the experimental results demonstrate that the algorithm proposed in this study reduces the location error by 8%. Furthermore, logistics and distribution costs are reduced by 11 %, contributing to the promotion of sustainable and environmentally friendly logistics operations.

Enhanced Green Logistics: Sustainable Distribution and Warehousing with IMU Positioning

Ecological Chemistry and Engineering S

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The objective of the green logistics distribution model is to minimise environmental pollution and energy usage by employing clean energy, optimising transport...

Enhanced Green Logistics: Sustainable Distribution and Warehousing with IMU Positioning

Published Online: Jul 10, 2024

Page range: 225 - 241

DOI: https://doi.org/10.2478/eces-2024-0016

Keywords
green logistics distribution, IMU, visual, adaptive Kalman filtering

© 2024 Xiangfeng Wu, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Enhanced Green Logistics: Sustainable Distribution and Warehousing with IMU Positioning

Published Online: Jul 10, 2024

Page range: 225 - 241

DOI: https://doi.org/10.2478/eces-2024-0016

Keywordsgreen logistics distribution, IMU, visual, adaptive Kalman filtering

© 2024 Xiangfeng Wu, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
green logistics distribution, IMU, visual, adaptive Kalman filtering