遇上动态遮挡：基于几何引导可变形卷积与跨尺度注意力的动态遮挡场景视觉SLAM算法YDS-SLAM
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Merged summary
TL;DR - YDS-SLAM is a dynamic-scene RGB-D visual SLAM pipeline that upgrades a YOLOv12 segmentation frontend with geometry-guided deformable convolution (GGDC) and cross-scale attention (CSA), then filters dynamic feature points and inpaints occluded background, reporting lower absolute pose error than DS-SLAM and DynaSLAM on TUM high-dynamic sequences.
- Frontend segmentation: GGDC is embedded in the YOLOv12 backbone, predicting 9×2 sampling offsets from a lightweight 2-conv+BN branch and fusing them with Sobel-extracted depth gradients so the receptive field deforms along object contours; CSA sits in the FPN neck, using global average pooling plus a learned weight matrix and Sigmoid to produce per-level attention weights, with transposed-conv upsampling / strided-conv downsampling for scale alignment.
- Dynamic point filtering: Rather than discarding all mask-interior points, it combines a depth-consistency residual (reprojecting inverse-projected 3D points via relative pose, thresholded) with an epipolar motion-consistency check (point-to-epipolar-line distance vs. threshold) to recover "pseudo-dynamic" static points and reject true dynamic ones.
- Background repair: Optimized local static map points are reprojected into the current frame via the world-to-camera pose and intrinsics to fill RGB and depth holes left by dynamic-object removal, improving map completeness.
- Results: Ablations on COCO show GGDC and CSA each help and combine best, beating Deeplabv3+ and Mask-RCNN; on TUM W_halfsphere/W_rpy/W_static/W_xyz, YDS-SLAM tracks ground truth more closely than ORB-SLAM3, DynaSLAM, and DS-SLAM. Note: the provided text has the specific mAP and RMSE improvement percentages blanked out, so exact gains cannot be quoted. Hardware: i5-13400F, 32GB RAM, RTX 5060, Ubuntu 20.04.
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遇上动态遮挡：基于几何引导可变形卷积与跨尺度注意力的动态遮挡场景视觉SLAM算法YDS-SLAM
TL;DR - YDS-SLAM is a dynamic-scene RGB-D visual SLAM pipeline that upgrades a YOLOv12 segmentation frontend with geometry-guided deformable convolution (GGDC) and cross-scale attention (CSA), then filters dynamic feature points and inpaints occluded background, reporting lower absolute pose error than DS-SLAM and DynaSLAM on TUM high-dynamic sequences.
- Frontend segmentation: GGDC is embedded in the YOLOv12 backbone, predicting 9×2 sampling offsets from a lightweight 2-conv+BN branch and fusing them with Sobel-extracted depth gradients so the receptive field deforms along object contours; CSA sits in the FPN neck, using global average pooling plus a learned weight matrix and Sigmoid to produce per-level attention weights, with transposed-conv upsampling / strided-conv downsampling for scale alignment.
- Dynamic point filtering: Rather than discarding all mask-interior points, it combines a depth-consistency residual (reprojecting inverse-projected 3D points via relative pose, thresholded) with an epipolar motion-consistency check (point-to-epipolar-line distance vs. threshold) to recover "pseudo-dynamic" static points and reject true dynamic ones.
- Background repair: Optimized local static map points are reprojected into the current frame via the world-to-camera pose and intrinsics to fill RGB and depth holes left by dynamic-object removal, improving map completeness.
- Results: Ablations on COCO show GGDC and CSA each help and combine best, beating Deeplabv3+ and Mask-RCNN; on TUM W_halfsphere/W_rpy/W_static/W_xyz, YDS-SLAM tracks ground truth more closely than ORB-SLAM3, DynaSLAM, and DS-SLAM. Note: the provided text has the specific mAP and RMSE improvement percentages blanked out, so exact gains cannot be quoted. Hardware: i5-13400F, 32GB RAM, RTX 5060, Ubuntu 20.04.