Jan Ruppelt

A novel finite state machine based step detection technique for pedestrian navigation systems

In this paper we present a novel finite state machine based step detection technique for precise personal navigation solutions with a foot-mounted inertial measurement unit (IMU). Generally, step detection methods are used to improve the navigation solution by applying Zero Velocity Updates (ZUPTs) in the navigation filter. All step detection techniques distort the navigation solution if ZUPTs are utilized at wrong times. Our approach based on a finite state machine is able to detect different stances of the foot with high accuracy. Therefore, Zero Velocity Updates can be applied in time and positively affect the precision of the navigation solution. The functionality of the step detection module in combination with a constraint, stochastic cloning (SC) Kalman filter are analyzed with real sensor data recorded with our pedestrian navigation system. Even with ultra-low cost inertial sensors, this new approach can clearly increase the accuracy of pedestrian navigation systems compared to state-of-the-art approaches.

Navigation Aiding by a Hybrid Laser-Camera Motion Estimator for Micro Aerial Vehicles.

Micro Air Vehicles (MAVs) equipped with various sensors are able to carry out autonomous flights. However, the self-localization of autonomous agents is mostly dependent on Global Navigation Satellite Systems (GNSS). In order to provide an accurate navigation solution in absence of GNSS signals, this article presents a hybrid sensor. The hybrid sensor is a deep integration of a monocular camera and a 2D laser rangefinder so that the motion of the MAV is estimated. This realization is expected to be more flexible in terms of environments compared to laser-scan-matching approaches. The estimated ego-motion is then integrated in the MAV’s navigation system. However, first, the knowledge about the pose between both sensors is obtained by proposing an improved calibration method. For both calibration and ego-motion estimation, 3D-to-2D correspondences are used and the Perspective-3-Point (P3P) problem is solved. Moreover, the covariance estimation of the relative motion is presented. The experiments show very accurate calibration and navigation results.

High-precision and robust indoor localization based on foot-mounted inertial sensors

In this paper we present a high-precision and robust indoor navigation system based on foot-mounted inertial sensors. We use a finite state machine based step detection technique for precise localization. This approach is able to detect different stances of the foot with high accuracy. The FSM-based step detection technique precisely detect Zero Velocity Updates (ZUPTs). ZUPTs can be applied in time in the navigation filter and positively affect the grade of the navigation solution. The functionality of the step detection module in combination with a constraint, stochastic cloning (SC) Kalman filter are analyzed with real sensor data recorded with our personal navigation system. Even with ultra-low cost inertial sensors, this approach delivers a personal navigation system for precise positioning in indoor environments and outdoor areas.

Stereo-camera visual odometry for outdoor areas and in dark indoor environments

A major goal in the research and development area of navigation is to improve the accuracy and the integrity of navigational systems. Scientists and researchers in this field are working on increasing the performance of navigational algorithms while the sensors for navigation systems become smaller and cheaper. Nonetheless ring laser gyroscope (RLG) and fiber optic gyroscope (FOG) are navigational components of the high price segment. For ultra-low-cost navigation systems RLGs and FOGs are too expensive at the present time, so that predominantly MEMS IMUs (microelectromechanical system inertial measurement units) are integrated into such systems. Due to the strong drift of MEMS sensors, aiding sensors have to be used to guarantee the integrity of the system and to get a longterm stable navigation solution. To limit inertial navigation error in outdoor scenarios the usage of Global Navigation Satellite System (GNSS) is an opportunity to deal with this problem. However, it is obvious that the use of GNSS is constrained by the limited availability of satellite signals in urban canyons or indoor situations. Especially close to buildings, GNSS signals are not always available and the information of the satellite signal can be negatively impacted due to multipath and shading. This is why optical sensors like laser range finders or cameras are frequently used as aiding sensors in urban areas or inside buildings.

Motion monitoring based on a finite state machine for precise indoor localization

This paper presents a precise stance detection method for accurate personal localization using a foot-mounted inertial measurement unit. The exact classification of the stance phases of the foot is realized with a finite state machine (FSM), which separates the human gait circle in different sub-states. The FSM-based approach provides high accurate and robust detections of Zero Velocity Updates (ZUPTs) which can be applied to the navigation filter. We use a constraint stochastic cloning (SC) Kalman filter to show the performance of the high precise ZUPT intervals with real world sensor data including forward, backward and staircase motion. Even for the movement type running and the signals of an ultra-low cost inertial measurement unit we achieve with our motion monitoring system a position estimation with an average error of less than 1.5% of the travelled distance.

Personal localization of task force members in urban environments

This paper presents a tightly-coupled INS/GNSS integration for personal navigation systems using foot-mounted sensors. Our precise relative positioning INS based on high accurate Zero-Velocity-Updates (ZUPTs) is fused with GNSS pseudo-range and doppler measurements for absolute position and heading estimation. Unique is the decision depending on the classified motion state if doppler measurements improve the accuracy of the navigation solution. In addition, the integrity of the GNSS signals are checked. The good knowledge of our precise relative positioning inertial system is used to detect and exclude GNSS measurements with high multipath errors. This is essential for robust personal localization in dense urban environments. Detailed analysis of a test walk in urban canyons with two outdoor-indoor transitions show the high performance and robustness of our proposed approach. Our system is appropriate for the localization of task force members due to the accuracy and robustness of the real-time capable navigation solution and the independence of pre-installed infrastructure.

Model independent control of a quadrotor with tiltable rotors: IEEE/ION PLANS 2016, April 11–14, Savannah, Georgia, United States of America

In order to enable autonomous flights and to increase the field of application of Micro Aerial Vehicles (MAVs) a new quadrotor with tiltable rotors is developed. This highly non-linear system requires complex algorithms to be controlled. To gain an efficient and robust control algorithm is a key problem concerning autonomous flights. In this article a simple but robust control approach based on control allocation is presented. This approach is used in order to keep the computational costs and the level of needed system information as low as possible. By tuning the controller properly with an algorithm from the field of bionics and applying a standard downhill optimization after that the robustness and performance are increased. Simulation results show the performance of the controller in different optimization stages and display the controllers robustness in the final stage.