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.

We present a novel approach to improve 6 degree-of-freedom state propagation for unmanned aerial vehicles in a classical filter through pre-processing of high-speed inertial data with AI algorithms. We evaluate both an LSTM-based approach as well as a Transformer encoder architecture. Both algorithms take as input short sequences of fixed length N of high-rate inertial data provided by an inertial measurement unit (IMU) and are trained to predict in turn one pre-processed IMU sample that minimizes the state propagation error of a classical filter across M sequences. This setup allows us to provide sufficient temporal history to the networks for good performance while maintaining a high propagation rate of pre-processed IMU samples important for later deployment on real-world systems. In addition, our network architectures are formulated to directly accept input data at variable rates thus minimizing necessary data preprocessing. The results indicate that the LSTM based architecture outperforms the Transformer encoder architecture and significantly improves the propagation error even for long IMU propagation times.