The capability to extract task specific, semantic information from raw sensory data is a crucial requirement for many applications of mobile robotics. Autonomous inspection of critical infrastructure with Unmanned Aerial Vehicles (UAVs), for example, requires precise navigation relative to the structure that is to be inspected. Recently, Artificial Intelligence (AI)-based methods have been shown to excel at extracting semantic information such as 6 degree-of-freedom (6-DoF) poses of objects from images. In this paper, we propose a method combining a state-of-the-art AI-based pose estimator for objects in camera images with data from an inertial measurement unit (IMU) for 6-DoF multi-object relative state estimation of a mobile robot. The AI-based pose estimator detects multiple objects of interest in camera images along with their relative poses. These measurements are fused with IMU data in a state-of-the-art sensor fusion framework. We illustrate the feasibility of our proposed method with real world experiments for different trajectories and number of arbitrarily placed objects. We show that the results can be reliably reproduced due to the self-calibrating capabilities of our approach.

翻译：从原始传感数据中提取任务特定的语义信息是移动机器人诸多应用的关键需求。例如，利用无人机对关键基础设施进行自主检测时，需要相对于待检测结构进行精确定位导航。近年来，基于人工智能的方法在从图像中提取语义信息（如物体的六自由度位姿）方面展现出卓越性能。本文提出一种融合方法，将先进的基于AI的相机图像中物体位姿估计器与惯性测量单元数据相结合，用于移动机器人的六自由度多目标相对状态估计。该AI位姿估计器可检测相机图像中多个感兴趣目标及其相对位姿，这些测量值与IMU数据在先进的传感器融合框架中实现融合。我们通过不同轨迹和任意放置目标数量的真实实验验证了所提方法的可行性。研究表明，得益于方法的自标定能力，实验结果具有可重复性。