Robust Electromagnetic Pose Estimation for Robotic Applications

Abstract

A wireless electromagnetic field-based sensor system is proposed, which enables the tracking of moving objects, e.g., drones. The gathered up to 6-degrees of freedom information is complementary to existing sensing principles, e.g., global positioning system (GPS) or vision-based systems. In addition, it can be used for stand-alone navigation or noninvasive localization of medical devices inside the human body. The sensor system is comprised of an exciter and a sensor. The exciter can be mounted on a moving robot and generates an electromagnetic field. The field is measured by the sensor, and subsequently, the pose of the exciter with respect to the sensors' pose is estimated. Conductive objects in the vicinity of the sensor alter the measured magnetic field due to the induced eddy currents. In general, unmanned aerial vehicles or wheeled robots mainly consist of conductive materials, which cause a significant estimation error. This article introduces an interference-aware electromagnetic near-fieldbased pose estimation approach. Specifically, the change in the magnetic field due to close conductive and ferromagnetic objects is modeled. Iterative numerical solutions of Maxwell's equations, based on, e.g., finite-element method, are avoided. Instead, an analytic expression of the change in the magnetic field due to present eddy currents is given. The advantages of the proposed concept for model-based low-complexity pose estimation concepts are shown using an extended Kalman filter. It is observed that the tracking performance using the introduced model outperforms the traditional model in eddy current scenarios significantly.

Proximity Perception in Human-Centered Robotics: A Survey on Sensing Systems and Applications

“…Consequently, the system can also be used to detect non-conductive and conductive objects and offers a very high measurement rate of 25 kHz. With multiple coils, inductive sensors can not only be used to obtain a distance estimate but full 6 DoF information, as discussed in [109].…”

Adaptive Recursive Decentralized Cooperative Localization for Multirobot Systems With Time-Varying Measurement Accuracy

“…Multi-robot systems have been widely used in many applications, such as area exploration [1], [2], region surveillance [3], and fast search and rescue [4]. Accurate positions and orientations (poses) of all the robots are essential for the success of their operation in performing various tasks [5], [6]. For an environment with accurate and persistent absolute 1 measurements, it is easy to achieve highly accurate estimates of robots' poses.…”

Combining extended Kalman filtering and rapidly-exploring random tree: An improved autonomous navigation strategy for four-wheel steering vehicle in narrow indoor environments

“…In the process of four-wheel steering vehicle positioning, the position and posture of the vehicle estimated by the odometer and electronic compass will result in positioning uncertainty due to the inherent measurement error of the odometer and electronic compass, and this uncertainty will increase over time. The update steps of Kalman filter 5,6 can filter errors well and improve the accuracy of autonomous navigation of four-wheel steering vehicles.…”