State estimation is a critical foundational module in robotics applications, where robustness and performance are paramount. Although in recent years, many works have been focusing on improving one of the most widely adopted state estimation methods, visual inertial odometry (VIO), by incorporating multiple cameras, these efforts predominantly address synchronous camera systems. Asynchronous cameras, which offer simpler hardware configurations and enhanced resilience, have been largely overlooked. To fill this gap, this paper presents VINS-Multi, a novel multi-camera-IMU state estimator for asynchronous cameras. The estimator comprises parallel front ends, a front end coordinator, and a back end optimization module capable of handling asynchronous input frames. It utilizes the frames effectively through a dynamic feature number allocation and a frame priority coordination strategy. The proposed estimator is integrated into a customized quadrotor platform and tested in multiple realistic and challenging scenarios to validate its practicality. Additionally, comprehensive benchmark results are provided to showcase the robustness and superior performance of the proposed estimator.

翻译：状态估计是机器人应用中的关键基础模块，其鲁棒性与性能至关重要。尽管近年来许多研究致力于通过引入多相机来改进最广泛采用的状态估计方法之一——视觉惯性里程计（VIO），但这些工作主要针对同步相机系统。异步相机因其更简单的硬件配置和更强的抗干扰能力而具有显著优势，却长期被忽视。为填补这一空白，本文提出VINS-Multi——一种面向异步相机的新型多相机-IMU状态估计器。该估计器包含并行前端、前端协调器以及能够处理异步输入帧的后端优化模块，通过动态特征点数量分配与帧优先级协调策略实现帧数据的有效利用。所提出的估计器被集成至定制化四旋翼平台，并在多个真实且具有挑战性的场景中进行测试以验证其实用性。此外，本文提供了全面的基准测试结果，以证明该估计器的鲁棒性与卓越性能。