Infrastructure-Aided Localization and State Estimation for Autonomous Mobile Robots

A slip-aware localization framework is proposed for mobile robots experiencing wheel slip in dynamic environments. The framework fuses infrastructure-aided visual tracking data (via fisheye lenses) and proprioceptive sensory data from a skid-steer mobile robot to enhance accuracy and reduce variance of the estimated states. The slip-aware localization framework includes: the visual thread to detect and track the robot in the stereo image through computationally efficient 3D point cloud generation using a region of interest; and the ego motion thread which uses a slip-aware odometry mechanism to estimate the robot pose utilizing a motion model considering wheel slip. Covariance intersection is used to fuse the pose prediction (using proprioceptive data) and the visual thread, such that the updated estimate remains consistent. As confirmed by experiments on a skid-steer mobile robot, the designed localization framework addresses state estimation challenges for indoor/outdoor autonomous mobile robots which experience high-slip, uneven torque distribution at each wheel (by the motion planner), or occlusion when observed by an infrastructure-mounted camera. The proposed system is real-time capable and scalable to multiple robots and multiple environmental cameras

From data acquisition to data fusion : a comprehensive review and a roadmap for the identification of activities of daily living using mobile devices

This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user’s daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs)

Off-line evaluation of indoor positioning systems in different scenarios: the experiences from IPIN 2020 competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001

Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

Three-Dimensional Beam Tracking for Wireless Communication Systems

The narrow millimetre wave beam in future 5G networks is easily interrupted by the movement of mobile handsets. In this thesis, a novel three dimensional (3-D) beam tracking method is proposed to achieve beam alignment between the access node (AN) and the user node (UN). The algorithm is achieved through rotation tracking and location tracking of the UN relative to AN's beam angle and location. The rotation tracking algorithm is operating within the location tracking algorithm to track the UN's rotation and the rotation data is used to update the location information as the UN moves. For rotation tracking, a gradient descent algorithm is employed for self-rotation tracking based on measurements obtained by the three smartphone sensors (gyroscope, accelerometer and magnetometer) embedded in the micro-electro-mechanical system (MEMS). For location tracking, an extended Kalman Fltering (EKF) based location tracking algorithm is also incorporated into the design by combining the data from the direction of arrival (DoA) and time of arrival (ToA) estimation results of the user node (UN) since accurate UN location information is also crucial in the beam tracking process. Moreover, an operation protocol is provided for the tracking process and tested in three different scenarios: self-rotation with a fixed UN position and one AN, self-rotation with the straight-line movement of the UN with one AN and self-rotation with the straight-line movement of six ANs

Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001.Peer reviewe

Indoor Positioning and Navigation

In recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot

All over the place localization system

The MAP-i Doctoral Programme in Informatics, of the Universities of Minho, Aveiro and PortoA localização é normalmente obtida utilizando um sistema de navegação baseado num ambiente estruturado. No entanto, estes sistemas não funcionam ou são difíceis de serem implantados em ambientes densos. Assim, considerando que as pessoas se deslocam geralmente a pé, neste trabalho é proposto um Sistema de Navigação Inercial para Pedestres (PINS). Nesta tese são identificadas as principais vantagens e desvantagens dos PINS, bem como, os algoritmos que estão na base destes sistemas. O objetivo é fornecer uma perspectiva abrangente sobre o que é necessário para desenvolver um PINS e quais os problemas encontrados mais frequentemente durante o seu desenvolvimento. São também identificados e comparados os sistemas e tecnologias mais importantes da literatura. Duas unidades de medição inercial foram desenvolvidas, sendo que os sensores inerciais foram combinados com sensores de força para melhorar a detecção das diferentes fases (fase de apoio e fase de balanço) da marcha humana, assim como, para ter uma informação mais precisa sobre a força de contacto. É muito importante que a fase de apoio seja devidamente detectada. Assim três diferentes algoritmos, utilizando diferentes sensores e métodos de fusão sensorial, são explicados e avaliados. A marcha humana representa um padrão que é repetido ao longo do tempo, o qual é aprendido utilizando algoritmos de aprendizagem com base nos dados obtidos pelas diferentes fontes de informação para realizar uma caracterização do passo. Esta caracterização leva a uma melhoria no desempenho do sistema, uma vez que os erros sistemáticos podem ser aprendidos, para depois serem corrigidos em tempo real. Como neste sistema existe mais do que uma fonte de informação, além das técnicas de fusão sensorial, são também aplicadas técnicas de fusão de informação. Depois dos dados serem obtidos com o equipamento desenvolvido, e do passo ser caracterizado com os dados aprendidos, são aplicados os algoritmos que fazem a estimativa do deslocamento. A arquitetura proposta é avaliada em quatro cenários de utilização real, dentro de um edifício, envolvendo diferentes tipos de caminhadas. Esta arquitectura levou a uma melhoria significativa da precisão da estimativa do deslocamento.Nowadays location information is typically obtained using a navigation system based on a structured environment. However, these systems do not work or are very difficult to be deployed in dense environments. Thus, considering that persons are usually on foot, in this work is proposed a Pedestrian Inertial Navigation System (PINS). In this thesis are identified the main advantages/disadvantages about PINS, as well as, the algorithms that are the base of this type of systems. It is provided a good insight about what is necessary to create a PINS and the problems that are encountered during its development. To complement these insights the fundamentals about Human Gait are presented, along with the main sensor and information fusion strategies used in this type of system. Also, the most important systems and technologies are identified and compared. Two inertial measurement units were developed, where the inertial sensors were combined with force sensors to improve the detection of different phases (stance and swing phase) of the human gait, as well as, to have proper information about the contact force. The stance phase is very important to be properly detected, therefore, three different algorithms using different sensors and sensor fusion methods are explained and evaluated. The human gait cycle represents a pattern that is a repeatable over time. Thus, this pattern is learned using machine learning algorithms, which are applied to the data obtained from the different data sources to perform a step characterization. This characterization leads to an improvement on the system’s performance, since the systematic errors can be learned to then be corrected in real-time. Since there is more than one source of information, besides sensor fusion techniques, it was also implemented an information fusion strategy. After collecting the data with the developed hardware and characterize the step according to the learned data, it is demonstrated the developed displacement estimation architecture. The proposed architecture and algorithms are evaluated through four real use case scenarios in a typical indoor environment involving different types of walking paths. This architecture led to a significant improvement on the displacement estimation accuracy.This work is funded by the ERDF (European Regional Development Fund) through the COMPETE Programme and by the Portuguese Government through the FCT (Portuguese Foundation for Science and Technology) within the doctoral grant SFRH/BD/70248/2010