测绘学报 ›› 2024, Vol. 53 ›› Issue (6): 1057-1076.doi: 10.11947/j.AGCS.2024.20230259
郭迟1,2,3(), 刘阳1, 罗亚荣2, 刘经南2, 张全2
收稿日期:
2023-09-08
发布日期:
2024-07-22
作者简介:
郭迟(1983—)男,博士,教授,主要从事北斗技术应用、无人系统智能导航及位置服务理论方法的研究。 E-mail:guochi@whu.edu.cn
基金资助:
Chi GUO1,2,3(), Yang LIU1, Yarong LUO2, Jingnan LIU2, Quan ZHANG2
Received:
2023-09-08
Published:
2024-07-22
About author:
GUO Chi (1983—), male, PhD, professor, majors in the application of BeiDou technology, intelligent navigation of unmanned systems, and theoretical methods of location services. E-mail: guochi@whu.edu.cn
Supported by:
摘要:
视觉同步定位与建图(visual simultaneous localization and mapping,VSLAM)技术以相机为主要传感器采集图像数据,基于多视几何、状态估计等算法原理获取载体的位置和姿态,同时构建一张用于导航定位的地图。视觉SLAM是自动驾驶、AR(augmented reality)、VR(virtual reality)、MR(mix reality)、智能机器人、无人机飞控中的关键技术。近年来,随着各个产业对智能导航定位的需求日渐增多,原本以几何测量为主的视觉SLAM逐渐融入对环境的语义理解。语义信息是指能够被人类直观感受和理解的概念,而图像语义信息是指图像中物体的轮廓、类别、显著性等信息。相比于图像中的几何特征,语义信息更具时空一致性,且更贴近人类感知的结果。将图像语义信息引入视觉SLAM,既能促进系统各个模块的性能,还能够提升视觉SLAM的智能感知能力,形成集几何测量、定位定姿、环境理解等多种功能的视觉语义SLAM。本文根据图像语义信息的应用方式,对视觉语义SLAM经典方案和最新研究进展进行归纳梳理。在此基础上,本文总结了视觉语义SLAM的现存问题与挑战,指出该领域未来的研究方向,以推动其面向智能导航定位进一步发展。
中图分类号:
郭迟, 刘阳, 罗亚荣, 刘经南, 张全. 图像语义信息在视觉SLAM中的应用研究进展[J]. 测绘学报, 2024, 53(6): 1057-1076.
Chi GUO, Yang LIU, Yarong LUO, Jingnan LIU, Quan ZHANG. Research progress in the application of image semantic information in visual SLAM[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(6): 1057-1076.
表1
图像语义信息的形式与经典方法"
语义获取方法 | 语义形式 | 实例 | 非实例 | 轮廓 | 经典方法 |
---|---|---|---|---|---|
目标检测 | 物体的矩形检测框、类别及分类置信度 | √ | × | × | Faster-RCNN[ |
语义分割 | 每个像素的类别及分类置信度 | × | √ | √ | FCN[ |
实例分割 | 物体的像素区域掩码、类别及分类置信度 | √ | × | √ | Mask-RCNN[ |
全景分割 | 可数物体与不可数物体的像素区域掩码、类别及分类置信度 | √ | √ | √ | Panoptic Segmentation[ |
视觉显著性检测 | 图像中每个像素的显著性评分 | — | — | — | SalGAN[ |
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