Dynamic Visual SLAM Integrating Depth-Consistency Segmentation and Progressive Distillation Network

SUN Jin; XU Tianyile; ZHAO Haitao

doi:10.11999/JEIT260266

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SUN Jin, XU Tianyile, ZHAO Haitao. Dynamic Visual SLAM Integrating Depth-Consistency Segmentation and Progressive Distillation Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260266

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SUN Jin, XU Tianyile, ZHAO Haitao. Dynamic Visual SLAM Integrating Depth-Consistency Segmentation and Progressive Distillation Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260266

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Dynamic Visual SLAM Integrating Depth-Consistency Segmentation and Progressive Distillation Network

doi: 10.11999/JEIT260266 cstr: 32379.14.JEIT260266

SUN Jin^{1, 2
,
,},
XU Tianyile^1
,,
ZHAO Haitao^1
,

1.
College of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2.
State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Funds: National Natural Science Foundation of China under Grant 62203231, the Open Fund of State Key Laboratory of Ocean Engineering under Grant GKZD010084, the Postgraduate Research & Practice Innovation Program of Jiangsu Province under Grant SJCX25_0374.

Received Date: 2026-03-11
Accepted Date: 2026-09-28
Rev Recd Date: 2026-09-28

Available Online: 2026-10-10

Abstract

Abstract

Objective Visual Simultaneous Localization and Mapping (SLAM) is a fundamental technology for autonomous navigation and environmental perception in Global Navigation Satellite System (GNSS)-denied environments. However, conventional visual SLAM systems generally rely on a static-scene assumption. In dynamic environments containing pedestrians, vehicles, and other moving objects, dynamic feature mismatches may violate geometric constraints and introduce unstable observations into pose estimation, resulting in trajectory drift or tracking degradation. Existing dynamic SLAM methods commonly incorporate semantic priors to suppress such interference. Semantic segmentation-based methods can provide fine-grained pixel-level dynamic regions, but usually require relatively high computational cost. In contrast, object detection-based methods provide higher inference efficiency, but their rectangular detection boxes inevitably contain static background regions, which may lead to the erroneous removal of valid static features and cause an over-masking problem. This issue becomes more pronounced in texture-poor or crowded scenes where sufficient static features are important for maintaining stable geometric constraints. To alleviate the trade-off between dynamic-feature filtering accuracy and computational efficiency, this paper proposes Depth-Consistency and Progressive-Distillation SLAM (DCPD-SLAM), a lightweight dynamic visual SLAM system for real-time state estimation under moderate computational resources. Methods DCPD-SLAM adopts a parallel dual-thread architecture comprising semantic perception and visual SLAM tracking. Semantic inference and feature extraction run concurrently, with frame-level synchronization before dynamic-feature filtering to align semantic results with the current Red-Green-Blue-Depth (RGB-D) frame. First, a depth-aware masking strategy refines coarse detection boxes. For selected non-rigid dynamic targets, such as pedestrians, valid depth pixels within the Region of Interest (ROI) are used to estimate a robust reference depth. The reference depth combines the median and the lower 33rd percentile of valid depth measurements to reduce the influence of background pixels and isolated depth noise. An adaptive depth-consistency threshold is then used to generate an initial foreground mask. When the valid-depth ratio is insufficient, the original detection box is retained as a fallback to maintain filtering reliability. Connected Component Analysis (CCA) and morphological dilation suppress isolated noise and compensate for boundary leakage, yielding a compact pixel-level pseudo-mask. Other predefined dynamic categories retain detection-box-based filtering to avoid additional mask-generation overhead.Second, a lightweight detector, Light-DEIM, reduces semantic perception costs. Partial Convolution (PConv) is incorporated into shallow C2f-FasterBlock stages to reduce spatial redundancy, while deeper C2f-Inverted Residual Mobile Block (iRMB) modules with window attention preserve semantic representations. Balanced width scaling further reduces model complexity while retaining sufficient high-level semantic capacity. Finally, progressive knowledge distillation compensates for representation loss caused by model lightweighting. Masked Generative Distillation (MGD) aligns intermediate features, while DETRDistill-based logits distillation aligns teacher–student predictions. During the first 70% of training epochs, Light-DEIM is trained with ground-truth supervision and feature and output distillation from a frozen DEIM-N teacher. For the remaining epochs, the distillation branches are disabled, and the student is optimized solely with ground-truth labels. Results and Discussions Experiments are conducted on the TUM RGB-D and Bonn RGB-D dynamic datasets. For the detection network, Light-DEIM reduces the parameter count from 4.0 M to 3.0 M and the computational complexity from 7.1 Giga Floating-point Operations (GFLOPs) to 5.6 GFLOPs. Without knowledge distillation, the lightweight model achieves an $ \text{mAP}_{50-95}^{\text{val}} $ of 36.2%. After progressive knowledge distillation, the $ \text{mAP}_{50-95}^{\text{val}} $increases to 41.9%, approaching the 42.5% performance of the DEIM-N teacher model while maintaining a validation throughput of 323 Frames Per Second (FPS) (Table 1). Ablation experiments further show that PConv mainly contributes to computational efficiency, whereas iRMB and Window Attention improve semantic representation. The combination of feature-level and output-level distillation provides better accuracy recovery than either distillation term alone (Table 2, Table 3).For SLAM evaluation, DCPD-SLAM achieves an Absolute Trajectory Error (ATE) of 0.019 $ \pm $0.002 $ 0.019\pm 0.002 $ m on the highly dynamic walking_xyz sequence, compared with 0.387 m for ORB-SLAM3 (Table 4). The trajectory-error curves and error distributions further indicate relatively stable localization performance in highly dynamic scenes (Fig. 5, Fig. 6). In four representative dynamic sequences, the proposed depth-aware masking strategy achieves an average dynamic-point rejection rate of 95.4% while limiting the static-point erroneous rejection rate to 12.4% (Table 6), indicating that it can reduce unnecessary removal of static background features while maintaining effective dynamic-feature filtering. At the complete-system level, DCPD-SLAM achieves average processing rates of 24.71 FPS and 27.11 FPS on the TUM RGB-D and Bonn RGB-D datasets, respectively (Table 7), demonstrating real-time processing capability on the moderate-compute Graphics Processing Unit (GPU) platform. Conclusions DCPD-SLAM balances localization accuracy and computational efficiency in dynamic RGB-D environments. Depth-aware masking mitigates over-masking from detection-box filtering, preserving static background features. Light-DEIM and progressive knowledge distillation reduce semantic perception overhead while retaining detection capability for dynamic-feature filtering. Experiments demonstrate real-time processing and stable localization on a moderate-compute GPU platform. However, pseudo-mask quality depends on depth measurements, with potential degradation under severe depth noise, strong illumination changes, reflective or transparent surfaces, distant targets, and severe occlusions. Future work will explore Light Detection and Ranging (LiDAR)-visual fusion to improve adaptability and measurement stability in complex, large-scale environments.
- Visual SLAM,
- Dynamic environments,
- Depth-aware mask,
- Lightweight network,
- Knowledge distillation

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References(27)

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