MOLO-SLAM: A Semantic SLAM for Accurate Removal of Dynamic Objects in Agricultural Environments

Jinhong Lv, Beihuo Yao, Haijun Guo, Changlun Gao, Weibin Wu (), Junlin Li, Shunli Sun and Qing Luo
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Jinhong Lv: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Beihuo Yao: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Haijun Guo: Guangdong Topsee Technology Co., Ltd., Guangzhou 510663, China
Changlun Gao: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Weibin Wu: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Junlin Li: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Shunli Sun: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Qing Luo: College of Engineering, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2024, vol. 14, issue 6, 1-27

Abstract: Visual simultaneous localization and mapping (VSLAM) is a foundational technology that enables robots to achieve fully autonomous locomotion, exploration, inspection, and more within complex environments. Its applicability also extends significantly to agricultural settings. While numerous impressive VSLAM systems have emerged, a majority of them rely on static world assumptions. This reliance constrains their use in real dynamic scenarios and leads to increased instability when applied to agricultural contexts. To address the problem of detecting and eliminating slow dynamic objects in outdoor forest and tea garden agricultural scenarios, this paper presents a dynamic VSLAM innovation called MOLO-SLAM (mask ORB label optimization SLAM). MOLO-SLAM merges the ORBSLAM2 framework with the Mask-RCNN instance segmentation network, utilizing masks and bounding boxes to enhance the accuracy and cleanliness of 3D point clouds. Additionally, we used the BundleFusion reconstruction algorithm for 3D mesh model reconstruction. By comparing our algorithm with various dynamic VSLAM algorithms on the TUM and KITTI datasets, the results demonstrate significant improvements, with enhancements of up to 97.72%, 98.51%, and 28.07% relative to the original ORBSLAM2 on the three datasets. This showcases the outstanding advantages of our algorithm.

Keywords: VSLAM; inspection robot; 3D reconstruction; dynamic confidence; dynamic object removal (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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