Georg Scholz

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.

Model Based Control of a Quadrotor with Tiltable Rotors

Micro Aerial Vehicles (MAV) with vertical takeoff and landing capabilities such as quadrotors are often used as sensor platforms. The carried equipment like cameras or LASER range finders has to be aligned to some point of interest. In this article a modified type of a quadrotor will be presented: a quadrotor with tiltable rotors which in contrast to common quadrotors is able to perform independent velocity and attitude movements. This ability makes additional aligning equipment to move the payload redundant. After a system description, the used control algorithm based on Nonlinear Inverse Dynamics (NID) is explained. In this article an extension of this approach is presented. The pseudo control hedging method removes the influence of the actuator dynamics from the control loop. The extension and its integration into the control algorithm are explained and the influence on the quality of control is demonstrated by simulation results.

A novel guidance and navigation system for MAVs capable of autonomous collision-free entering of buildings

Micro Aerial Vehicles for autonomous explorations of hazardous areas are predestined to support emergency and rescue forces. Especially the autonomous access to buildings is highly demanding due to insufficient GNSS reception in urban terrain and narrow passageways into buildings. Thus, this paper presents a complete flight system, consisting of guidance, navigation and control subsystems. All these elements are designed to enable safe flights into buildings. The guidance subsystem is divided into two parts. The vision based guidance part is manoeuvring the MAV on an intermediate position in front of the building. The potential field based guidance part enables the MAV to fly inside the buildings without having any collisions. For that, neither any prior knowledge about the building structure, nor any maps are necessary. To provide the flight guidance with information about the actual kinematic state of the MAV an accurate and robust navigation system not depending on GNSS measurements is used. The complete system is evaluated using simulated flight data.

Inertial sensor data based motion estimation aided by image processing and differential barometry

In this paper, a navigation filter is presented that can be used for pedestrian navigation or autonomous micro aerial vehicles (MAV) in the context of search and rescue missions. The proposed system does not rely on external infrastructure and does not make any geometric assumptions on the environment. Point to point navigation tasks are addressed as these are typical for search and rescue scenarios. Therefore, a high accuracy odometry approach is applied. The proposed system fuses sensor data of a mono camera, an IMU and a barometer. In particular, a filter-based approach to visual inertial odometry (VIO) is extended by the fusion with a barometer. Additionally, a tightly coupling between image processing and filter state is applied such that a robust feature tracking and a standstill detection is achieved. The proposed system is evaluated with both simulated and real world datasets. The navigation solution is estimated consistently at IMU rate. The final position error is below one percent of the distance traveled on real world datasets.

Collision avoidance system with situational awareness capabilities for autonomous MAV indoor flights

Micro Aerial Vehicles (MAVs) lend themselves to perform supporting reconnaissance flights and provide rescue forces with important information beyond their own field of vision. In this paper, we present an auto-adaptive collision avoidance system to aid indoor reconnaissance missions of autonomous MAVs by means of situational awareness. Efficient multi-sensor indoor classification and landmark detection on the fly serve an Artificial Potential Field (APF) based motion control. In applying supplemental potentials tailored to the specific needs of the detected surrounding situations, control factor calculation remains merely reactive. Yet, the resulting virtual force field will imply simultaneous obstacle avoidance and intelligent flight control. The system is depending only on data from camera, 2D laser rangefinder and inertial measurement unit. Neither a data link to the ground station nor any training data is required to independently explore accessible interior space in unknown environment. Finally, availability for use is validated by both simulated and real flight data.

Indoor laser-based SLAM for micro aerial vehicles

This article presents a laser-based 2D simultaneous localization and mapping (SLAM) algorithm for indoor environments. An adaption and optimization of a ground vehicle SLAM solution (TinySLAM) for the use with Micro Aerial Vehicles is proposed. Optimizations of the map update strategy and a motion model improves the accuracy strongly. An extension to 3D mapping is introduced. The presented algorithm is tested with simulated and real world data. The optimized SLAM solution maps a whole floor of an office building very accurately and achieves embedded real-time capability.

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.