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，利用测距传感器和视觉-惯性里程计（VIO）的互补优势实现定位。我们采用两个固定滞后平滑器（FLS）来解耦精确状态估计与闭环控制所需的高速位姿生成。第一个FLS将超宽带（UWB）测距测量与VIO相结合，以估计机器人轨迹及在杂乱环境中影响测距的系统偏差。第二个FLS通过估计VIO平滑修正量，生成用于在线控制的高速位姿估计。该方法计算轻量，可在计算受限的微型飞行器（MAV）上运行。我们通过在多个真实杂乱室内环境中进行的动态轨迹闭环飞行测试验证了该方法的有效性。使用现成传感器即可实现分米级至亚分米级定位精度，并借助最小化调参的开源控制器达到分米级跟踪精度。