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摘要: 从提高机器人视觉同时定位与地图构建（Visual simultaneous localization and mapping，VSLAM）算法的实时性出发，在VSLAM的视觉里程计中提出一种自适应特征地图配准的算法.首先，针对视觉里程计中特征地图信息冗余、耗费计算资源的问题，划分特征地图子区域并作为结构单元，再根据角点响应强度指标大小提取子区域中少数高效的特征点，以较小规模的特征地图配准各帧：针对自适应地图配准时匹配个数不满足的情况，提出一种区域特征点补充和特征地图扩建的方法，快速实现该情形下当前帧的再次匹配：为了提高视觉里程计中位姿估计的精度，提出一种帧到帧、帧到模型的g2o（General graph optimization）特征地图优化模型，更加有效地更新特征地图的内点和外点.通用数据集的实验表明，所提方法的定位精度误差在厘米级，生成的点云地图清晰、漂移少，相比于其他算法，具有更好的实时性、定位精度以及建图能力.Abstract: An improved visual simultaneous localization and mapping (VSLAM) algorithm based on the adaptive feature map is proposed in order to enhance real-time performance. The feature map is divided into sub-regions and structural units are employed to reduce computation cost. After that the most effective feature points, sorted by corner response intensity, are extracted and matched with the current frame. In the case that the adaptive map features are not enough for registration, a method of adding more region feature point supplements and extending the feature map is also proposed, which enables re-matching ability for the visual odometry system. A frame-to-frame and frame-to-map graph optimization method is also implemented to effectively update the internal and external points in the feature map. The results in public dataset show that the location accuracy error of the proposed method is centimeter and that the point cloud map is clear and has less drift. Compared with the original one, the proposed method has better real-time performance, positioning accuracy and the ability to build maps.1) 本文责任编委 黄庆明
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图 11 各算法构建的3D点云地图对比
Fig. 11 Comparison of 3D point cloud maps based on different algorithms
图 13 freiburg2-slam数据集的轨迹误差图和3D点云地图
Fig. 13 Trajectory error map and 3D point cloud map for freiburg2-slam data sets
表 1 不同算法的实时性、特征地图累计规模比较
Table 1 Comparison of real time and feature map cumulative size of each algorithm
$T$(ms), $k$(个) RGBD-SLAM-V2 FVO 文献[15] 本文算法(1) 本文算法(2) fr1-xyz $52.31/11.88 \times {10^5}$ $44.11/6.12 \times {10^5}$ $46.07/6.13 \times {10^5}$ ${\bf{43.54}}/{\bf{3.03{\rm{ \times }}{10^5}}}$ $43.81/{\bf{3.03{\rm{ \times }}{10^5}}}$ fr1-360 $59.86/11.09 \times {10^5}$ $51.90/6.37 \times {10^5}$ $55.54/6.37 \times {10^5}$ ${\bf{49.14}}/{\bf{3.08{\rm{ \times }}{10^5}}}$ $49.71/{\bf{3.08{\rm{ \times }}{10^5}}}$ fr1-room $48.07/9.14 \times {10^5}$ $40.17/5.59 \times {10^5}$ $46.70/5.59 \times {10^5}$ ${\bf{39.24}}/{\bf{2.55{\rm{ \times }}1{0^5}}}$ $40.26/{\bf{2.55{\rm{ \times }}{10^5}}}$ fr1-desk $50.69/9.37 \times {10^5}$ ${\bf{43.86}}/5.65 \times {10^5}$ $47.32/5.63 \times {10^5}$ $43.95/{\bf{2.73{\rm{ \times }}{10^5}}}$ $44.32/{\bf{2.73{\rm{ \times }}{10^5}}}$ fr1-desk2 $56.32/10.36 \times {10^5}$ $47.80/6.22 \times {10^5}$ $53.92/6.22 \times {10^5}$ ${\bf{46.51}}/{\bf{2.95{\rm{ \times }}{10^5}}}$ $47.95/{\bf{2.95{\rm{ \times }}{10^5}}}$ flfh $240.45/38.74 \times {10^5}$ $186.43/17.86 \times {10^5}$ $197.80/17.86 \times {10^5}$ ${\bf{181.89}}/{\bf{8.72{\rm{ \times }}{10^5}}}$ $183.14/{\bf{8.72{\rm{ \times }}{10^5}}}$ flnp $301.11/43.47 \times {10^5}$ $255.96/21.55 \times {10^5}$ $269.35/21.55 \times {10^5}$ ${\bf{248.07}}/{\bf{11.66{\rm{ \times }}{10^5}}}$ $249.51/{\bf{11.66{\rm{ \times }}{10^5}}}$ 
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表 2 不同算法的轨迹误差对比
Table 2 Comparison of trajectory errors of different algorithms
$E$ (m) RGBD-SLAM2 FVO 文献[15] 本文算法(1) 本文算法(2) fr1-xyz 0.019 0.024 0.017 0.016 ${\bf{0.013}}$ fr1-360 0.018 0.022 ${\bf{0.017}}$ 0.018 ${\bf{0.017}}$ fr1-room 0.239 0.286 0.073 0.082 ${\bf{0.072}}$ fr1-desk 0.038 0.084 0.026 0.028 ${\bf{0.025}}$ fr1-desk2 0.092 0.157 0.039 0.042 ${\bf{0.032}}$ flfh 0.466 0.764 0.228 0.241 ${\bf{0.151}}$ flnp 0.836 0.988 0.381 0.411 ${\bf{0.188}}$
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