Janne Haverinen

Global indoor self-localization based on the ambient magnetic field

There is evidence that animals utilize local anomalities of Earths magnetic field not just for orientation detection but also for true navigation, i.e., some animals are not only able to detect the direction of Earths magnetic field (compass heading), they are able to derive positional information from local cues arising from the local anomalities of Earths magnetic field. Similarly to Earths non-constant magnetic field, the magnetic field inside buildings can be highly non-uniform. The magnetic field fluctuations inside buildings arise from both natural and man-made sources, such as steel and reinforced concrete structures, electric power systems, electric and electronic appliances, and industrial devices. Assuming that the anomalities of the magnetic field inside a building are nearly static and they have sufficient local variability, the anomalies provide a unique magnetic fingerprint that can be utilized in global self-localization. Based on the evidence presented in this article it can be argued that this hypothesis is valid. In this article, a Monte Carlo Localization (MCL) technique based on the above hypothesis is proposed. The feasibility of the technique is demonstrated by presenting a series of global self-localization experiments conducted in four arbitrarily selected buildings, including a hospital. The experiment setup consists of a mobile robot instrumented with a 3-axis magnetometer and a computer. In addition to global robot self-localization experiments, successful person self-localization experiments were also conducted by using a wireless, wearable magnetometer. The reported experiments suggest that the ambient magnetic field may remain sufficiently stable for longer periods of time giving support for self-localization techniques utilizing the local deviations of the magnetic field.

Distributed road surface condition monitoring using mobile phones

The objective of this research is to improve traffic safety through collecting and distributing up-to-date road surface condition information using mobile phones. Road surface condition information is seen useful for both travellers and for the road network maintenance. The problem we consider is to detect road surface anomalies that, when left unreported, can cause wear of vehicles, lesser driving comfort and vehicle controllability, or an accident. In this work we developed a pattern recognition system for detecting road condition from accelerometer and GPS readings. We present experimental results from real urban driving data that demonstrate the usefulness of the system. Our contributions are: 1) Performing a throughout spectral analysis of tri-axis acceleration signals in order to get reliable road surface anomaly labels. 2) Comprehensive preprocessing of GPS and acceleration signals. 3) Proposing a speed dependence removal approach for feature extraction and demonstrating its positive effect in multiple feature sets for the road surface anomaly detection task. 4) A framework for visually analyzing the classifier predictions over the validation data and labels.

A global self-localization technique utilizing local anomalies of the ambient magnetic field

Magnetic field fluctuations in modern buildings arise from both natural and man-made sources, such as steel and reinforced concrete structures, electric power systems, electric and electronic appliances, and industrial devices. If the anomalies of the magnetic field inside the building are nearly static and they have sufficient local variability, they provide a unique magnetic fingerprint that can be utilized in global self-localization. In this article, a Monte Carlo Localization (MCL) technique based on this hypothesis is proposed. The feasibility of the technique is demonstrated by presenting a series of global localization experiments conducted in four arbitrarily selected buildings, including a hospital. The experiment setup consists of a mobile robot instrumented with a 3-axis magnetometer and a computer. In addition, successful human self-localization experiments were conducted by using a wireless wearable magnetometer. The reported experiments suggest that the ambient magnetic field may remain sufficiently stable for longer periods of time, giving support for self-localization techniques utilizing the local deviations of the field.

Simultaneous localization and mapping using ambient magnetic field

In this paper we propose a simultaneous localization and mapping (SLAM) method that utilizes local anomalies of the ambient magnetic field present in many indoor environments. We use a Rao-Blackwellized particle filter to estimate the pose distribution of the robot and Gaussian Process regression to model the magnetic field map. The feasibility of the proposed approach is validated by real world experiments, which demonstrate that the approach produces geometrically consistent maps using only odometric data and measurements obtained from the ambient magnetic field. The proposed approach provides a simple, low-cost, and space-efficient solution for solving the SLAM problem present in many domestic and swarm robotics application domains.

Magnetic field-based SLAM method for solving the localization problem in mobile robot floor-cleaning task

In this paper we present a SLAM method based on indoor magnetic field anomalies and measure the acquired map quality in the context of the localization problem present in mobile robot floor-cleaning scenarios. According to our real-world robot experiments in different environments, it appears that most modern buildings have sufficient magnetic field variation to make the method applicable in mobile robot floor-cleaning tasks. We show that our method can be used to acquire maps that are accurate enough to be utilized in the robot coverage problem, thus reducing over-cleaning. We use Gaussian Processes to model the magnetic field and a Rao-Blackwellized Particle Filter to estimate the pose distribution of the robot. Because magnetic field anomalies are not correlated to typical features used in localization, our method can handle many situations in which other methods fail. The minimalistic sensory requirements of our method make it a very viable alternative for low-cost domestic robots.

A geomagnetic field based positioning technique for underground mines

A geomagnetic field based positioning technique is proposed for underground mining environments. The proposed technique utilizes the anomalies of the geomagnetic field present in underground environments. The main source of the magnetic anomalies is the complex distribution of metallic minerals such as iron ore. The distribution of metallic minerals produces unique spatial magnetic patterns in underground mines which can be utilized for positioning. Preliminary results are presented using the data collected from Pyhäsalmi, which is an underground copper and zinc mine located in central Finland. The data used in the experiments were collected from tunnels located approximately 1400 meters below the surface. The obtained results suggest that the proposed positioning technique can provide pose estimates with an accuracy of ≈1.5meters. The proposed technique can potentially provide a robust, and a cost-efficient positioning solution for underground environments with minor infrastructure requirements

A Miniature Mobile Robot With a Color Stereo Camera System for Swarm Robotics Research

In swarm robotics research, instead of using large size robots, it is often desirable to have multiple small size robots for saving valuable work space and making the maintainance of the robots easier. Also, the implementation costs of a miniature robot is lower because of simpler mechanical design. In this paper, we present a novel modular miniature mobile robot designed for swarm robotics research. The sensor set of the robot includes a color stereo camera system with two CMOS cameras and DSP, allowing each robot to do sophisticated stereo image processing on-board. The modular design permits the addition of new modules into the system. The modules communicate using three serial buses (SPI, I2C, and UART), which enable flexible, adaptive, and fast inter-module data exchange. The robot is developed for swarm robotics research with the aim to provide a low-cost and low-power miniature mobile robot with capabilities typically found only in large size robots.

Entity Notation: enabling knowledge representations for resource-constrained sensors

The forthcoming ambient systems will contain a large amount of sensors. Representing the data produced by these sensors in a format suitable for ambient intelligence applications would enable a large number of useful services. However, such formats tend to require processing power and communication bandwidth not available in many sensors utilizing ultra low-power microcontrollers and radio chip solutions. This paper presents a lightweight data representation, Entity Notation, to tackle this problem. Sensors with limited computation and communication capabilities can use Entity Notation to describe the data they produce. Entity Notation can be transformed into knowledge representations in a straightforward manner, and hence, the data produced by sensor nodes can be utilized with ease by any ambient intelligence system compatible with the common knowledge representations. This paper presents the design of Entity Notation, its implementations on embedded sensors and the evaluation of its performance.

Near-optimal SLAM exploration in Gaussian processes

In this paper we examine near-optimal SLAM exploration in Gaussian processes. We propose a submodular sensing quality function that extends studies from discrete sensor placement to an autonomous sampling scheme where sensing sites must be visited frequently. This is beneficial in the SLAM context, where sensing sites themselves bear uncertainties. Also in time-critical applications, we have to balance modeling accuracy against sensing time, which introduces noisy samples with only limited replications at each site.

An obstacle detection system using a light stripe identification based method

A light stripe tracking and identification method is proposed for a structured light based obstacle detection system operating in an outdoor environment. The method makes the structured light based detection system more robust and applicable to use outdoors as aid for navigation. The method differentiates between the structured light produced by a light stripe projector and the light stripe kind patterns caused by ambient illumination. The centre of gravity of the segmented light stripe is tracked by using a Kalman filter. The position information together with the other properties of the stripe segment, including intensity, length and orientation are used to identify the same light stripe segment in adjacent images. By using a pulsed light source it is possible to differentiate between true and false light stripes depending on their time of appearance. During the project, a working obstacle detection system for a partly structured outdoor environment was implemented.