EndoSensorFusion：基于粒子滤波的内窥镜胶囊机器人切换状态空间模型多传感器数据融合
EndoSensorFusion: Particle Filtering-Based Multi-sensory Data Fusion with Switching State-Space Model for Endoscopic Capsule Robots
摘要：
可靠的实时的多传感器融合功能对于主动控制胶囊式内窥镜机器人的定位至关重要，胶囊式内窥镜机器人是一种新兴的胃肠道微创诊断和治疗技术。在这项研究中，我们提出了一种新的多传感器融合方法的基础上的粒子滤波器，采用了在线估计传感器的可靠性和非线性运动学模型学习的递归神经网络。我们的方法顺序估计真正的机器人姿态从多个传感器提供的噪声姿态观测。我们实验测试的方法，使用5度的自由度（5-DoF）的绝对姿态测量的磁定位系统和6-DoF的相对姿态测量的视觉里程计。此外，所提出的方法能够通过忽略损坏的数据来检测和处理传感器故障，从而提供医疗设备所期望的鲁棒性。详细的分析和评价，使用离体实验猪胃模型证明，我们的系统实现了不同类型的内窥镜胶囊机器人轨迹的高平移和旋转精度。
英文摘要：
A reliable, real time multi-sensor fusion functionality is crucial for localization of actively controlled capsule endoscopy robots, which are an emerging, minimally invasive diagnostic and therapeutic technology for the gastrointestinal (GI) tract. In this study, we propose a novel multi-sensor fusion approach based on a particle filter that incorporates an online estimation of sensor reliability and a non-linear kinematic model learned by a recurrent neural network. Our method sequentially estimates the true robot pose from noisy pose observations delivered by multiple sensors. We experimentally test the method using 5 degree-of-freedom (5-DoF) absolute pose measurement by a magnetic localization system and a 6-DoF relative pose measurement by visual odometry. In addition, the proposed method is capable of detecting and handling sensor failures by ignoring corrupted data, providing the robustness expected of a medical device. Detailed analyses and evaluations are presented using ex-vivo experiments on a porcine stomach model prove that our system achieves high translational and rotational accuracies for different types of endoscopic capsule robot trajectories.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
