We propose a fixed-lag smoother-based sensor fusion architecture to leverage the complementary benefits of range-based sensors and visual-inertial odometry (VIO) for localization. We use two fixed-lag smoothers (FLS) to decouple accurate state estimation and high-rate pose generation for closed-loop control. The first FLS combines ultrawideband (UWB)-based range measurements and VIO to estimate the robot trajectory and any systematic biases that affect the range measurements in cluttered environments. The second FLS estimates smooth corrections to VIO to generate pose estimates at a high rate for online control. The proposed method is lightweight and can run on a computationally constrained micro-aerial vehicle (MAV). We validate our approach through closed-loop flight tests involving dynamic trajectories in multiple real-world cluttered indoor environments. Our method achieves decimeter-to-sub-decimeter-level positioning accuracy using off-the-shelf sensors and decimeter-level tracking accuracy with minimally-tuned open-source controllers.

翻译：我们提出了一种基于固定滞后平滑器的传感器融合架构，以发挥基于距离的传感器与视觉-惯性里程计（VIO）在定位中的互补优势。我们采用两个固定滞后平滑器（FLS），分别实现精确状态估计与用于闭环控制的高速率位姿生成。第一个FLS融合了基于超宽带（UWB）的距离测量与VIO，以估计机器人轨迹及在复杂环境中影响距离测量的系统偏差。第二个FLS估计对VIO的平滑校正，从而为在线控制生成高速率位姿估计。所提方法轻量化，可在计算资源受限的微型飞行器（MAV）上运行。通过在多个真实复杂室内环境中进行的动态轨迹闭环飞行测试，验证了本方法的有效性。采用商用传感器即可实现分米至亚分米级定位精度，同时，利用最小调优的开源控制器即可获得分米级跟踪精度。