Liu Quanpan, Wang Zhengjie, Wang Huan
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel). 2020 Mar 9;20(5):1511. doi: 10.3390/s20051511.
In practical applications, how to achieve a perfect balance between high accuracy and computational efficiency can be the main challenge for simultaneous localization and mapping (SLAM). To solve this challenge, we propose SD-VIS, a novel fast and accurate semi-direct visual-inertial SLAM framework, which can estimate camera motion and structure of surrounding sparse scenes. In the initialization procedure, we align the pre-integrated IMU measurements and visual images and calibrate out the metric scale, initial velocity, gravity vector, and gyroscope bias by using multiple view geometry (MVG) theory based on the feature-based method. At the front-end, keyframes are tracked by feature-based method and used for back-end optimization and loop closure detection, while non-keyframes are utilized for fast-tracking by direct method. This strategy makes the system not only have the better real-time performance of direct method, but also have high accuracy and loop closing detection ability based on feature-based method. At the back-end, we propose a sliding window-based tightly-coupled optimization framework, which can get more accurate state estimation by minimizing the visual and IMU measurement errors. In order to limit the computational complexity, we adopt the marginalization strategy to fix the number of keyframes in the sliding window. Experimental evaluation on EuRoC dataset demonstrates the feasibility and superior real-time performance of SD-VIS. Compared with state-of-the-art SLAM systems, we can achieve a better balance between accuracy and speed.
在实际应用中，如何在高精度和计算效率之间实现完美平衡可能是同时定位与地图构建（SLAM）面临的主要挑战。为了解决这一挑战，我们提出了SD-VIS，一种新颖的快速且准确的半直接视觉惯性SLAM框架，它能够估计相机运动以及周围稀疏场景的结构。在初始化过程中，我们将预积分的惯性测量单元（IMU）测量值与视觉图像对齐，并基于基于特征的方法，利用多视图几何（MVG）理论校准出度量尺度、初始速度、重力向量和陀螺仪偏差。在前端，关键帧通过基于特征的方法进行跟踪，并用于后端优化和回环检测，而非关键帧则通过直接方法用于快速跟踪。这种策略使得系统不仅具有直接方法更好的实时性能，还具有基于特征的方法的高精度和回环检测能力。在后端，我们提出了一种基于滑动窗口的紧密耦合优化框架，通过最小化视觉和IMU测量误差可以获得更精确的状态估计。为了限制计算复杂度，我们采用边缘化策略来固定滑动窗口中的关键帧数。在EuRoC数据集上的实验评估证明了SD-VIS的可行性和卓越实时性能。与现有最先进的SLAM系统相比，我们能够在精度和速度之间实现更好的平衡。
