Xia Linlin, Meng Qingyu, Chi Deru, Meng Bo, Yang Hanrui
School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China.
School of Computer Science, Northeast Electric Power University, Jilin 132012, China.
Sensors (Basel). 2019 Apr 29;19(9):2004. doi: 10.3390/s19092004.
The development and maturation of simultaneous localization and mapping (SLAM) in robotics opens the door to the application of a visual inertial odometry (VIO) to the robot navigation system. For a patrol robot with no available Global Positioning System (GPS) support, the embedded VIO components, which are generally composed of an Inertial Measurement Unit (IMU) and a camera, fuse the inertial recursion with SLAM calculation tasks, and enable the robot to estimate its location within a map. The highlights of the optimized VIO design lie in the simplified VIO initialization strategy as well as the fused point and line feature-matching based method for efficient pose estimates in the front-end. With a tightly-coupled VIO anatomy, the system state is explicitly expressed in a vector and further estimated by the state estimator. The consequent problems associated with the data association, state optimization, sliding window and timestamp alignment in the back-end are discussed in detail. The dataset tests and real substation scene tests are conducted, and the experimental results indicate that the proposed VIO can realize the accurate pose estimation with a favorable initializing efficiency and eminent map representations as expected in concerned environments. The proposed VIO design can therefore be recognized as a preferred tool reference for a class of visual and inertial SLAM application domains preceded by no external location reference support hypothesis.
机器人技术中同步定位与地图构建（SLAM）技术的发展与成熟，为视觉惯性里程计（VIO）在机器人导航系统中的应用打开了大门。对于没有可用全球定位系统（GPS）支持的巡逻机器人，嵌入式VIO组件通常由惯性测量单元（IMU）和摄像头组成，它将惯性递推与SLAM计算任务相融合，使机器人能够在地图中估计自身位置。优化后的VIO设计亮点在于简化的VIO初始化策略以及前端基于融合点和线特征匹配的高效位姿估计方法。在紧密耦合的VIO结构中，系统状态以向量形式明确表示，并由状态估计器进一步估计。详细讨论了后端与数据关联、状态优化、滑动窗口和时间戳对齐相关的后续问题。进行了数据集测试和实际变电站场景测试，实验结果表明，所提出的VIO能够在相关环境中实现准确的位姿估计，具有良好的初始化效率和出色的地图表示。因此，在没有外部位置参考支持假设的情况下，所提出的VIO设计可被视为一类视觉和惯性SLAM应用领域的首选工具参考。
