A Lightweight Semantic Visual Simultaneous Localization and Mapping Framework for Inspection Robots in Dynamic Environments

YU Haoyang; LI Yansheng; XIAO Lingli; ZHOU Jiyuan

doi:10.11999/JEIT250301

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YU Haoyang, LI Yansheng, XIAO Lingli, ZHOU Jiyuan. A Lightweight Semantic Visual Simultaneous Localization and Mapping Framework for Inspection Robots in Dynamic Environments[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250301

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YU Haoyang, LI Yansheng, XIAO Lingli, ZHOU Jiyuan. A Lightweight Semantic Visual Simultaneous Localization and Mapping Framework for Inspection Robots in Dynamic Environments[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250301

Citation:

YU Haoyang, LI Yansheng, XIAO Lingli, ZHOU Jiyuan. A Lightweight Semantic Visual Simultaneous Localization and Mapping Framework for Inspection Robots in Dynamic Environments[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250301

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A Lightweight Semantic Visual Simultaneous Localization and Mapping Framework for Inspection Robots in Dynamic Environments

doi: 10.11999/JEIT250301 cstr: 32379.14.JEIT250301

1.
School of Artificial Intelligence (iFLYTEK Artificial Intelligence School, CQUPT), Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
School of Integrated Circuits (Chongqing International Semiconductor Institute), Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (52575102), Chongqing Urban Administration Bureau Project (Urban Management Science Section 2022-34), Chongqing Natural Science Foundation (CSTB2022NSCQ-MSX0340)

Received Date: 2025-04-25
Rev Recd Date: 2025-07-28

Available Online: 2025-08-04

Abstract

Abstract

Objective In complex dynamic environments such as industrial parks and urban roads, inspection robots depend heavily on visual Simultaneous Localization And Mapping (SLAM) systems. However, the presence of moving objects often causes feature drift and map degradation, reducing SLAM performance. Furthermore, conventional semantic segmentation models typically require extensive computational resources, rendering them unsuitable for embedded platforms with limited processing capabilities, thereby constraining SLAM deployment in autonomous inspection tasks. To address these challenges, this study proposes a lightweight semantic visual SLAM framework designed for inspection robots operating in dynamic environments. The framework incorporates a semantic segmentation-based dynamic feature rejection method to achieve real-time identification of dynamic regions at low computational cost, thereby improving SLAM robustness and mapping accuracy. Methods Building upon the 11th generation lightweight YOLO segmentation model (YOLOv11n-seg), a systematic lightweight redesign is implemented. to enhance performance under constrained computational resources. First, the original neck is replaced with DyCANet, a lightweight multi-scale feature fusion module that integrates dynamic point sampling and channel attention to improve semantic representation and boundary segmentation. DyCANet combines DySample, a dynamic upsampling operator that performs content-aware spatial sampling with minimal overhead, and ChannelAttention_HSFPN, a hierarchical attention structure that strengthens multi-scale integration and highlights critical semantic cues, particularly for small or occluded objects in complex scenes. Second, a Dynamic Convolution module (DynamicConv) is embedded into all C3k2 modules to enhance the adaptability and efficiency of feature extraction. Inspired by the Mixture-of-Experts framework, DynamicConv applies a conditional computation mechanism that dynamically adjusts kernel weights based on the input feature characteristics. This design allows the network to extract features more effectively across varying object scales and motion patterns, improving robustness against dynamic disturbances with low computational cost. Third, the original segmentation head is replaced by the Reused and Shared Convolutional Segmentation Head (RSCS Head), which enables decoder structure sharing across multi-scale branches. RSCS reduces redundant computation by reusing convolutional layers and optimizing feature decoding paths, further improving overall model efficiency while maintaining segmentation accuracy. These architectural modifications result in DHSR-YOLOSeg, a lightweight semantic segmentation model that significantly reduces parameter count and computational cost while preserving performance. DHSR-YOLOSeg is integrated into the tracking thread of ORB-SLAM3. to provide real-time semantic information. This enables dynamic object detection and the removal of unstable feature points during localization, thereby enhancing the robustness and trajectory consistency of SLAM in complex dynamic environments. Results and Discussions Ablation experiments on the COCO dataset demonstrate that, compared with the baseline YOLOv11n-seg, the proposed DHSR-YOLOSeg achieves a 13.8% reduction in parameter count, a 23.1% decrease in Giga Floating Point Operations (GFLOPs), and an approximate 2% increase in mean Average Precision at IoU 0.5 (mAP50) (Table 1). On the KITTI dataset, DHSR-YOLOSeg reaches an inference speed of 60.19 frame/s, which is 2.14% faster than YOLOv11n-seg and 275% faster than the widely used Mask R-CNN (Table 2). For trajectory accuracy evaluation on KITTI sequences 00～10, DHSR-YOLOSeg outperforms ORB-SLAM3 in 8 out of 10 sequences, achieving a maximum Root Mean Square Error (RMSE) reduction of 16.76% and an average reduction of 8.78% (Table 3). Compared with DynaSLAM and DS-SLAM, the proposed framework exhibits more consistent error suppression across sequences, improving both trajectory accuracy and stability. In terms of runtime efficiency, DHSR-YOLOSeg achieves an average per-frame processing time of 48.86 ms on the KITTI dataset, 18.44% and 41.38% lower than DS-SLAM and DynaSLAM, respectively (Table 4). The per-sequence processing time ranges from 41 to 55 ms, which is comparable to the 35.64 ms of ORB-SLAM3, indicating that the integration of semantic segmentation introduces only a modest computational overhead. Conclusions This study addresses the challenge of achieving robust localization for inspection robots operating in dynamic environments, particularly in urban road settings characterized by frequent interference from pedestrians, vehicles, and other moving objects. To this end, a semantic-enhanced visual SLAM framework is proposed, in which a lightweight semantic segmentation model, DHSR-YOLOSeg, is integrated into the stereo-based ORB-SLAM3 pipeline. This integration enables real-time identification of dynamic objects and removal of their associated feature points, thereby improving localization robustness and trajectory consistency. The DHSR-YOLOSeg model incorporates three key architectural components—DyCANet for feature fusion, DynamicConv for adaptive convolution, and the RSCS Head for efficient multi-scale decoding. Together, these components reduce the model’s size and computational cost while preserving segmentation performance, providing an efficient and deployable perception solution for resource-constrained platforms. Experimental findings show that: (1) ablation tests on the COCO dataset confirm substantial reductions in complexity with preserved accuracy, supporting embedded deployment; (2) frame rate comparisons on the KITTI dataset demonstrate superior performance over both lightweight and standard semantic segmentation methods, meeting real-time SLAM requirements; (3) trajectory evaluations indicate that the dynamic feature rejection strategy effectively mitigates localization errors in dynamic scenes; and (4) the overall system maintains high runtime efficiency, ensuring a practical balance between semantic segmentation and real-time localization performance. However, current experiments are conducted under ideal conditions using standardized datasets, without fully reflecting real-world challenges such as multi-sensor interference or unstructured environments. Moreover, the trade-offs between model complexity and accuracy for each lightweight module have not been systematically assessed. Future work will focus on multimodal sensor fusion and adaptive dynamic perception strategies to enhance the robustness and applicability of the proposed system in real-world autonomous inspection scenarios.
- Inspection robot,
- Semantic segmentation,
- Visual Simultaneous Localization And Mapping (SLAM),
- Dynamic feature filtering

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References(25)

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