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摘要: 作为移动机器人与自动驾驶领域的关键基础技术, 视觉同时定位与地图构建(V-SLAM)在动态环境中面临严峻挑战. 由动态物体引起的特征匹配错误常常导致定位偏差、地图失真以及系统鲁棒性受损. 运动分割技术是提高V-SLAM性能的重要手段, 但在复杂动态场景中准确区分静态和动态元素仍极具挑战性. 本文系统梳理V-SLAM运动分割研究进展, 根据对环境的潜在假设, 将现有方法分为三个主要研究范式, 并给出各范式的技术原理, 代表性策略的核心优势、本质局限及适用边界. 最后展望未来的研究方向.Abstract: As a critical foundational technology in the fields of mobile robotics and autonomous driving, visual simultaneous localization and mapping (V-SLAM) faces severe challenges in dynamic environments. Feature mismatches induced by dynamic objects frequently lead to localization drift, map distortion, and degradation of system robustness. Motion segmentation technology is an important means of enhancing V-SLAM performance, but accurate discrimination between static and dynamic elements in complex dynamic scenarios remains highly challenging. This paper systematically reviews the research progress on motion segmentation techniques for V-SLAM. Taxonomically categorizing existing methods into three primary research paradigms based on underlying environmental assumptions, we present the technical principles of each paradigm, along with the core strengths, inherent limitations, and applicability boundaries of representative strategies. Finally, future research directions are prospected.
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Key words:
- visual SLAM /
- dynamic environments /
- motion segmentation /
- motion understanding /
- multi-sensor fusion /
- mobile robot
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图 6 基于几何约束的动态特征点移除流程图
Fig. 6 Flowchart of dynamic feature point removal based on geometric constraints
图 7 RTDSLAM系统在真实动态环境中的实验结果
Fig. 7 Experimental results of the RTDSLAM system in a real dynamic environment
图 9 基于深度聚类与形态学约束的精炼掩码生成
Fig. 9 Refined mask generation based on deep clustering and morphological constraints
图 12 低纹理热红外图像的特征跟踪结果
Fig. 12 Feature tracking results for low-texture thermal infrared images
表 3 部分基于静态场景假设的运动分割方法
方法 绝对轨迹均方根误差(m) 相机类型 运行环境 基础框架 单帧跟踪时间(ms) Dou等[54] 0.01138 RGB-D i7-12700K + 32GB ORB-SLAM3 14.153 SamSLAM[55] 0.19870 RGB-D i7-12850HX + 32GB ORB-SLAM3 — DZ-SLAM[56] 0.01300 RGB-D i7-12700K + 32GB ORB-SLAM3 231 GMP-SLAM[57] 0.01300 RGB-D i7-9750H + 16GB ORB-SLAM2 50 Wang等[58] 0.01480 RGB-D i5-7500HQ + 16GB ORB-SLAM3 54 RED-SLAM[59] 0.01480 RGB-D i7-12700KF + 16GB ORB-SLAM3 26.88 Zhang等[60] 0.01300 RGB-D i7 13700K + 64GB ORB-SLAM2 169 DyGS-SLAM[61] 0.01400 RGB-D i7-9750H + 16GB ORB-SLAM3 50 FD-SLAM[62] 0.01620 RGB-D i7 + 16GB ORB-SLAM3 355.18 FND-SLAM[63] 0.01300 RGB-D i7-13650HX ESLAM 812.32 PLFF-SLAM[64] 0.08600 RGB-D i7-13650HX ORB-SLAM3 — Qi等[65] 0.01470 RGB-D i7- 12700 + RTX 4060TiORB-SLAM2 117.8 Cheng等[66] 0.01300 RGB-D i7-13700K + 64GB Manhattan-SLAM 169 Li等[67] 0.01490 RGB-D R9 + RTX3060 ORB-SLAM3 86 
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表 1 常用的动态环境下视觉SLAM数据集
Table 1 Common visual SLAM datasets in dynamic environments
数据集名称 场景 采集平台 LiDAR RGB MONO (单色) Stereo IMU Event GNSS TUM[39] 室内 手持/机器人 √ √ Bonn[40] 室内 手持 √ OpenLORIS[41] 室内 机器人 √ √ √ √ ADVIO[48] 室内/室外 手持 √ √ KITTI[44] 室外道路 汽车 √ √ √ √ √ Oxford[45] 室外道路 汽车 √ √ √ √ √ ICL-NUIM[42] 室内 手持 √ Augmented ICL-NUIM[43] 室内 手持 √ M2DGR[49] 室内/室外 机器人 √ √ √ √ √ √ RAWSEEDS[50] 室内/室外 机器人 √ √ √ √ EuRoC MAV[51] 室内/室外 飞行器 √ √ √ Mapillary Vistas[46] 室外 手持/机器人 √ √ Apollo Scape[47] 室外 汽车 √ √ Fusion PortableV2[52] 室内/室外 手持/机器人/汽车 √ √ √ √ √ M3DGR[53] 室内/室外 机器人 √ √ √ √
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表 2 静态场景假设方法的分类及特点
Table 2 Categories and characteristics of static scene assumption methods
类别 优点 缺点 基于相机运动的几何约束方法 通过几何约束区分静态与动态特征, 在低动态环境下精度较好 高动态环境下精度显著下降 基于直接几何约束的方法 无需估计相机运动, 计算量小且实时性高 动态特征占比高时分割精度低 基于静态背景构建方法 高动态场景精度较好 计算复杂
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表 4 代表性计算机视觉算法
Table 4 Representative computer vision algorithms
领域 方法模型 年份 贡献 目标检测 R-CNN[88] 2014 首次将CNN引入目标检测领域. Fast R-CNN[89] 2015 引入感兴趣区域池化层实现特征共享, 结合多任务损失端到端训练. Faster R-CNN[90] 2016 提出区域建议网络替代选择性搜索, 实现候选框生成与检测一体化. SSD[91] 2016 结合多尺度特征图预测与预设锚框机制. YOLO[78] 2016 开创单阶段检测范式. YOLOv5[92] 2022 集成自适应锚框计算、Mosaic自适应数据增强及自适应图片缩放. RT-DETRv3[93] 2025 设计了层次密集正监督方法, 通过CNN辅助分支和自注意力扰动策略. YOLOv13[94] 2025 引入超图自适应相关增强机制, 通过自适应超图计算建模高阶视觉相关性. 图像分割 FCN[95] 2015 首创全卷积网络结构. SegNet[79] 2017 利用最大池化索引实现高效上采样. Mask R-CNN[80] 2017 在Faster R-CNN基础上添加掩码分支并设计RoIAlign层消除量化误差. DeepLabv3 2018 改进空洞空间金字塔池化并引入图像级特征. YOLACT[81] 2019 提出原型掩码生成与掩码系数预测的并行分支结构. SegFormer[96] 2021 结合无位置编码的分层Transformer编码器和全MLP解码器. Mask2Former[97] 2022 提出通用图像分割架构, 通过掩码注意力机制和高效多尺度策略. Segment Anything[98] 2023 定义可提示分割任务, 支持点/框/文本等任意提示输入. MagNet[99] 2024 设计跨模态对齐损失和模块, 缩小语言—图像模态差距. OMG-Seg[100] 2024 整合多领域分割任务, 降低计算和参数开销. GleSAM[101] 2025 利用生成式潜在空间增强提高对低质量图像的鲁棒性.
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表 5 部分基于语义信息的动态SLAM方法
Table 5 Partially semantic-information-based dynamic SLAM methods
方法 语义帧选择 运动分割方法 相机类型 年份 运行环境 单帧跟踪时间/(ms) DS-SLAM[82] 每帧 SegNet RGB-D 2018 i7 + P4000 59 DynaSLAM[83] 每帧 MaskR-CNN+几何约束 单、双目及RGB-D 2018 TeslaM40 195 DynamicSLAM[103] 每帧 SSD 单目 2019 i5-7300HQ + GTX1050Ti 45 YPL-SLAM[110] 每帧 YOLOv5s RGB-D 2024 i7- 12700 + RTX206050~100 SDD-SLAM[112] 关键帧滑动窗口 GroundingDINO+SAM-Track RGB-D 2025 NVIDIA RTX3080 — HMC-SLAM[107] 每帧 YOLOv5 RGB-D 2025 i5 + RTX4070Ti 51.02 DOA-SLAM[104] 每帧 FastInst实例分割 立体相机 2025 — — DYMRO-SLAM[108] 关键帧 MaskR-CNN 双目 2025 — 64.89 DYR-SLAM[109] 每帧 YOLOv8 RGB-D 2025 i7-12700K + RTX3080 57.82 DEG-SLAM[105] 每帧 YOLOv5 RGB-D 2025 i5-8300H + GTX1050Ti 57.82 DHP-SLAM[106] 每帧 SOLOv2 RGB-D/双目 2025 i7-9750H + RTX2070 76.09 YOLO-SLAM[113] 每帧 Darknet19-YOLOv3 RGB-D 2021 Intel Core i5-4288U 696.09 RDS-SLAM[114] 双向关键帧 Mask R-CNN或SegNet RGB-D 2021 RTX2080Ti 22~30 RDMO-SLAM[115] 关键帧 Mask R-CNN RGB-D 2021 RTX 2080Ti 22~35 Jiang等[116] 每帧 RT-DETR with PP-LCNet RGB-D 2025 i7-12650H + RTX4060 29.86 Cheng等[117] 每帧 YOLOX RGB-D/双目 2024 E5-2686v4 + RTX3080 37.43 Huang等[118] 每帧 YOLOv5s RGB-D 2024 R7-6800H + RTX3050Ti 29.86 DFE-SLAM[119] 每帧 YOLOv5s RGB-D 2024 — — YLS-SLAM[120] 每帧 YOLOv5s-seg 单目/RGB-D 2025 i5 + RTX3060 17 UE-SLAM[122] 每帧 DINOv2 单目 2025 i7-12700K + RTX3090Ti —
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表 6 部分基于多传感器信息的SLAM方法
Table 6 Partially multi-sensor-information-based SLAM methods
方法 年份 传感器 绝对轨迹均方根
误差/(m)分割方法 运行环境 MSCKF[124] 2007 单目相机 + IMU − 多状态约束Kalman滤波、特征点跟踪与三角测量 T7200 VINS-Mono[123] 2018 单目相机 + IMU 0.12 0.22 关键帧选择 + 滑动窗口优化 + 特征点跟踪 i7- 4790 VINS-Fusion[157] 2022 RGB(单目/双目)+ IMU + GPS 0.06 滑动窗口优化 + 因子图优化 + 多传感器因子融合 − R3LIVE[144] 2021 LiDAR + RGB + IMU 0.085 基于点云运动一致性与视觉语义融合 i7-8550U + 8GB PLD-VINS[129] 2019 RGB-D相机 + IMU 0.731866 改进EDLines检测线特征, 光流法跟踪线特征 XeonE5645 + 48GB VA-fusion[147] 2023 RGB-D相机 + 麦克风阵列+IMU 0.301 声源方向投影至图像平面, 直接标记动态区域 i7 + 64GB DP-VINS[130] 2023 立体相机 + IMU 0.092 通过光流场聚类和残差计算估计运动状态 i7-9700K + 16GB FMSCKF[132] 2024 单目相机 + IMU 0.151 基于关键帧特征跟踪阈值, 结合IMU预积分预测匹配 i7-11800H + 32GB RDynaSLAM[143] 2024 4D毫米波雷达 + 相机 0.233 通过RANSAC提取动态簇并生成动态掩码, 以过滤动态点 E3-1270V2 + 8GB PSMD-SLAM[148] 2024 LiDAR + 相机 + IMU 2.87 概率传播 + PCA聚类 + 全景分割辅助动态检测 5950X + RTX3090 Ground-Fusion[158] 2024 RGB-D相机 + IMU + 轮速计 + GNSS 0.1 运动一致性检查、深度验证 + 传感器异常检测 E3-1270V2 + 8GB SFCI[136] 2025 单目相机 + IMU 0.22 IMU预积分预测位姿生成对极几何约束, 直接剔除动态点 i9 + 16GB RAM EN-SLAM[150] 2024 RGB-D + 事件相机 0.1597 利用事件数据的高动态范围和时序特性 RTX 4090 WTI-SLAM[155] 2025 热红外相机 + IMU 0.059 多尺度相位一致性特征提取 + 光流前后向跟踪 i5-12450H + 32GB Hybrid-VINS[159] 2025 UBSL + 单目相机 + IMU 0.11 基于深度一致性过滤动态物体 − DVI-SLAM[160] 2025 单目/stereo相机 + IMU 0.148 动态融合视觉 + 惯性因子优化位姿 RTX3090
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