Ultra-wideband (UWB) based positioning with fewer anchors has attracted significant research interest in recent years, especially under energy-constrained conditions. However, most existing methods rely on discrete-time representations and smoothness priors to infer a robot's motion states, which often struggle with ensuring multi-sensor data synchronization. In this article, we present a continuous-time UWB-Inertial-Odometer localization system (CT-UIO), utilizing a non-uniform B-spline framework with fewer anchors. Unlike traditional uniform B-spline-based continuous-time methods, we introduce an adaptive knot-span adjustment strategy for non-uniform continuous-time trajectory representation. This is accomplished by adjusting control points dynamically based on movement speed. To enable efficient fusion of {inertial measurement unit (IMU) and odometer data, we propose an improved extended Kalman filter (EKF) with innovation-based adaptive estimation to provide short-term accurate motion prior. Furthermore, to address the challenge of achieving a fully observable UWB localization system under few-anchor conditions, the virtual anchor (VA) generation method based on multiple hypotheses is proposed. At the backend, we propose an adaptive sliding window strategy for global trajectory estimation. Comprehensive experiments are conducted on three self-collected datasets with different UWB anchor numbers and motion modes. The result shows that the proposed CT-UIO achieves 0.403m, 0.150m, and 0.189m localization accuracy in corridor, exhibition hall, and office environments, yielding 17.2%, 26.1%, and 15.2% improvements compared with competing state-of-the-art UIO systems, respectively. The codebase and datasets of this work will be open-sourced at https://github.com/JasonSun623/CT-UIO.

翻译：近年来，基于超宽带（UWB）的少锚点定位技术，尤其在能量受限条件下，引起了广泛的研究关注。然而，现有方法大多依赖离散时间表示和平滑先验来推断机器人运动状态，往往难以保证多传感器数据的同步性。本文提出了一种连续时间UWB-惯性-里程计定位系统（CT-UIO），采用基于较少锚点的非均匀B样条框架。与传统基于均匀B样条的连续时间方法不同，我们引入了一种自适应节点跨度调整策略，用于非均匀连续时间轨迹表示，通过根据运动速度动态调整控制点实现。为实现惯性测量单元（IMU）与里程计数据的高效融合，我们提出了一种改进的扩展卡尔曼滤波器（EKF），采用基于新息的自适应估计，以提供短期精确的运动先验。此外，针对少锚点条件下实现UWB定位系统完全可观测性的挑战，提出了基于多假设的虚拟锚点（VA）生成方法。在后端，我们提出了一种自适应滑动窗口策略用于全局轨迹估计。在三个自采集数据集上进行了综合实验，这些数据集具有不同的UWB锚点数量和运动模式。结果表明，所提出的CT-UIO在走廊、展厅和办公室环境中分别实现了0.403米、0.150米和0.189米的定位精度，相较于当前先进的UIO系统，分别提升了17.2%、26.1%和15.2%。本工作的代码库和数据集将在https://github.com/JasonSun623/CT-UIO开源。