Lasse Klingbeil

Transforming Agriculture through Pervasive Wireless Sensor Networks

A large-scale, outdoor pervasive computing system uses static and animal-borne nodes to measure the state of a complex system comprising climate, soil, pasture, and animals. Agriculture faces many challenges, such as climate change, water shortages, labor shortages due to an aging urbanized population, and increased societal concern about issues such as animal welfare, food safety, and environmental impact. Humanity depends on agriculture and water for survival, so optimal, profitable, and sustainable use of our land and water resources is critical.

A Wireless Sensor Network for Real-Time Indoor Localisation and Motion Monitoring

This paper describes the development and deployment of a wireless sensor network for monitoring human motion and position in an indoor environment. Mobile sensor nodes comprising mote-type devices, along with inertial sensors are worn by persons moving inside buildings. Motion data is preprocessed onboard mobile nodes and transferred to a static network of seed nodes using a delay tolerant protocol with minimal radio packet overhead. A Monte Carlo based localisation algorithm is implemented, which uses a persons pedometry data, indoor map information and seed node positions to provide accurate, real-time indoor location information. The performance of the network protocols and localisation algorithm are evaluated using simulated and real experimental data.

Detecting walking activity in cardiac rehabilitation by using accelerometer

This study is part of the ongoing care assessment platform project, which involves monitoring vital signs and daily activity profile information of chronic disease patients undergoing cardiac rehabilitation. In this study, we have focussed on detecting walking activity from a cardiac rehab session which includes many other high intensity activities such as biking and rowing, using waist worn accelerometers. Walking is an important measure useful to assess the mobility of elderly people. Various methods have been proposed in the literature to identify walking from waist worn accelerometer signals based on wavelet, frequency and computational intelligence methods. Wavelet based approach, due to its feasibility to be implemented in real time with low computational complexity, good accuracies and also the ability to provide good time frequency resolution, has been the most desirable approach. In this study, we have evaluated and compared six wavelet decomposition based measures to detect walk from other high intensity activities. The different measures were derived using anterior-posterior, vertical, medio-lateral and signal vector magnitude (SVM) acceleration signals. The results show that all these measures can discriminate walking from other high intensity activities and the SVM based measure was the most efficient (89.14% sensitivity and 89.97 % specificity).

A modular and mobile system for indoor localization

The work presents a system for sensor data and complementary information fusion for localization in indoor environments. The system is based on modular sensor units, which can be attached to a person and contains various sensors, such as range sensors, inertial and magnetic sensors, a GPS receiver and a barometer. The measurements are processed using Bayesian Recursive Estimation algorithms and combined with available a priori knowledge such as map information or human motion models and constraints. The processing can be done locally, since all necessary data are available on the mobile unit. This system provides a platform for implementation, combination and evaluation of various localization principles and can be used for a variety of applications, such as indoor and outdoor pedestrian navigation, localization of other objects such as vehicles as well as robotics applications.

Real-Time Single-Frequency GPS/MEMS-IMU Attitude Determination of Lightweight UAVs

In this paper, a newly-developed direct georeferencing system for the guidance, navigation and control of lightweight unmanned aerial vehicles (UAVs), having a weight limit of 5 kg and a size limit of 1.5 m, and for UAV-based surveying and remote sensing applications is presented. The system is intended to provide highly accurate positions and attitudes (better than 5 cm and 0.5∘) in real time, using lightweight components. The main focus of this paper is on the attitude determination with the system. This attitude determination is based on an onboard single-frequency GPS baseline, MEMS (micro-electro-mechanical systems) inertial sensor readings, magnetic field observations and a 3D position measurement. All of this information is integrated in a sixteen-state error space Kalman filter. Special attention in the algorithm development is paid to the carrier phase ambiguity resolution of the single-frequency GPS baseline observations. We aim at a reliable and instantaneous ambiguity resolution, since the system is used in urban areas, where frequent losses of the GPS signal lock occur and the GPS measurement conditions are challenging. Flight tests and a comparison to a navigation-grade inertial navigation system illustrate the performance of the developed system in dynamic situations. Evaluations show that the accuracies of the system are 0.05∘ for the roll and the pitch angle and 0.2∘ for the yaw angle. The ambiguities of the single-frequency GPS baseline can be resolved instantaneously in more than 90% of the cases.

Multi-modal sensor data and information fusion for localization in indoor environments

The work presents the development of a framework for sensor data and complementary information fusion for localization in indoor environments. The framework is based on a modular and flexible sensor unit, which can be attached to a person and which contains various sensor types, such as range sensors, inertial and magnetic sensors or barometers. All measurements are processed within Bayesian Recursive Estimation algorithms and combined with available a priori knowledge such as map information or human motion models.

A study on indoor pedestrian localization algorithms with foot-mounted sensors

The work presents a foot-mounted sensor system for a combined indoor/outdoor pedestrian localization. The approach is based on a zero-velocity update scheme formulated as an Extended or Unscented Kalman filter with quaternion orientation representation and employs a custom low-cost sensor unit. Both filters are compared in terms of speed and accuracy on a representative trajectory. A detailed discussion is provided with respect to different filter state formulations, stance still detection mechanisms and associated filter parameters. The presented pure inertial system is augmented with magnetic field measurements for heading correction. The challenging localization scenario with an elevator is addressed by augmenting the system with a barometric pressure sensor for height error correction. The work also demonstrates how the basic algorithm version can be extended with reference systems such as GPS and passive RFID tags on the floor for absolute position drift correction.

Direct Georeferencing of Micro Aerial Vehicles – System Design, System Calibration and First Evaluation Tests

& TIAN 2011), infrastructure inspection (MERZ & KENDOUL 2011) or surveying (EISENBEISS et al. 2005)UAVs are meanwhile often deployed. Recently, there has been a discussion concerning the term UAV. Since this paper is particularly dealing with lightweight UAVs the more specific termMAV (micro aerial vehicle) will be used throughout this paper. MAVs can generally be characterized having a weight limit of 5 kg and a size limit of 1.5 m (EISENBEISS 2009).

Efficient orientation estimation algorithm for low cost inertial and magnetic sensor systems

The presented work develops a high-dynamic quaternion attitude estimation Unscented Kalman Filter suitable for the implementation in low cost sensor systems, comprising accelerometers, gyroscopes and magnetic field sensors. The adoption of a spherical σ-point selection strategy and the implementation of the square-root version reduces the requirements for computational resources. A special handling of angular rate, gyroscope bias, and translational accelerations within the process model is implemented and evaluated. Issues regarding the quaternion mean calculations, noise representation as well as control noise scaling are discussed. The performance of the designed filter is assessed using real hand motion reference data and correspondingly generated noisy sensor measurements.

Towards Automated Large-Scale 3D Phenotyping of Vineyards under Field Conditions

In viticulture, phenotypic data are traditionally collected directly in the field via visual and manual means by an experienced person. This approach is time consuming, subjective and prone to human errors. In recent years, research therefore has focused strongly on developing automated and non-invasive sensor-based methods to increase data acquisition speed, enhance measurement accuracy and objectivity and to reduce labor costs. While many 2D methods based on image processing have been proposed for field phenotyping, only a few 3D solutions are found in the literature. A track-driven vehicle consisting of a camera system, a real-time-kinematic GPS system for positioning, as well as hardware for vehicle control, image storage and acquisition is used to visually capture a whole vine row canopy with georeferenced RGB images. In the first post-processing step, these images were used within a multi-view-stereo software to reconstruct a textured 3D point cloud of the whole grapevine row. A classification algorithm is then used in the second step to automatically classify the raw point cloud data into the semantic plant components, grape bunches and canopy. In the third step, phenotypic data for the semantic objects is gathered using the classification results obtaining the quantity of grape bunches, berries and the berry diameter.