基于目标检测的单目视觉半稠密语义地图构建

传统的视觉SLAM系统在机器人定位和制图工作中取得了显著的成功，但存在着缺乏场景信息、地图过于稀疏、单目相机初始化困难等亟待解决的问题。本文提出了MNS-SLAM（Monocular-semantic SLAM），将目标检测算法与单目视觉SLAM（同时定位与地图构建）技术相结合，进而构建有助于环境理解的半稠密语义地图。首先，通过目标检测网络YOLOv4检测对象获取边界框和类别信息，通过消失点算法和二次曲面恢复算法由2D目标检测恢复出3D长方体及二次曲面，实现3D物体的位姿初始化。同时，引入了目标间相对位姿不变性的语义约束，构造了语义损失函数，将其添加到BA优化中，最后通过增量式3D线段提取，构建带有物体语义信息的半稠密地图。文中方法在TUM公开数据集和真实场景中进行试验，不仅构建了半稠密地图，同时添加了语义信息，为后端的优化提供了新的约束，相机的绝对和相对位姿误差表现出优于单目ORB-SLAM2的性能，有助于搭载单目相机的移动机器人感知和理解环境，执行更复杂的任务。

Semi-dense Semantic Monocular SLAM Based on Object Detection

Traditional visual SLAM systems have achieved remarkable success in robot localization and mapping work, but there are pressing problems such as lack of scene information, too sparse maps, and difficulties in initializing monocular cameras. In this paper, we propose MNS-SLAM (Monocular-semantic SLAM), which combines object detection algorithms with monocular visual SLAM (simultaneous localization and map construction) techniques, and then constructs semi-dense semantic maps that contribute to environmental understanding. First, the bounding box and category information are obtained through the object detection network YOLOv4 detection object, and the 3D cube and quadrics are recovered from 2D object detection by the vanishing point algorithm and quadric recovery algorithm to realize the initialization of the 3D object''s pose. Meanwhile, the semantic constraint of relative bit-pose invariance among targets is introduced, the semantic loss function is constructed and added to BA optimization, and finally the semi-dense map with object semantic information is constructed by incremental 3D line segment extraction. The method in the paper is tested on the TUM public dataset and real scenarios, which not only constructs semi-dense maps, but also adds semantic information to provide new constraints for back-end optimization, and the absolute and relative positional errors of the cameras show better performance than monocular ORB-SLAM2, which helps mobile robots equipped with monocular cameras to perceive and understand the environment and perform more complex tasks.