Low cost embedded multimodal opto-inertial human motion tracking system

Human motion tracking systems are widely used in various application spaces, such as motion capture, rehabilitation, or sports. There exists a number of such systems in the State-Of-The-Art (SOA) that vary in price, complexity, accuracy and the target applications. With the continued advances in system integration and miniaturization, wearable motion trackers gain in popularity in the research community. The opto-inertial trackers with multimodal sensor fusion algorithms are some of the common approaches found in SOA. However, these trackers tend to be expensive and have high computational requirements. In this work, we present a prototype version of our opto-inertial, motion tracking system that offers a low-cost alternative. The 3D position and orientation are determined by fusing optical and inertial sensor data together with knowledge about two external reference points using a purpose-designed data fusion algorithm. An experimental validation was carried out on one of the use cases that this system is intended for, i.e. barbell squat in strength training. The results showed that the total RMSE in position and orientation was 32.8 mm and 0.89 degree, respectively. It operated in real-time at 20 frames per second

Human-Robot SLAM in industrial environments

A novel approach to the SLAM problem has been tested in an industrial environment within a robotic assistance context. In order to be fully reliable in non-modelled circumstances where the environment cannot be considered as known a priori, a robot assistant must be able to localize and map its environment. The use of a camera sensor to solve localization has several advantages and weaknesses due the nature of the only-bearing data. But as the robot is expected to assist the human agent, this agent can deploy additional sensors and provide the robot with data to help solve the SLAM problem. Thus, another camera worn by the human agent is used to produce non-continuous stereo data with the robotic camera, to speed-up and add robustness to several parts of the monocular SLAM process considered. The approach has been tested with real experiments focused on singular trajectories and other issues found on industrial environments.Postprint (published version

Human-Robot SLAM in industrial environments

A novel approach to the SLAM problem has been tested in an industrial environment within a robotic assistance context. In order to be fully reliable in non-modelled circumstances where the environment cannot be considered as known a priori, a robot assistant must be able to localize and map its environment. The use of a camera sensor to solve localization has several advantages and weaknesses due the nature of the only-bearing data. But as the robot is expected to assist the human agent, this agent can deploy additional sensors and provide the robot with data to help solve the SLAM problem. Thus, another camera worn by the human agent is used to produce non-continuous stereo data with the robotic camera, to speed-up and add robustness to several parts of the monocular SLAM process considered. The approach has been tested with real experiments focused on singular trajectories and other issues found on industrial environments

Human-Robot SLAM in industrial environments

A novel approach to the SLAM problem has been tested in an industrial environment within a robotic assistance context. In order to be fully reliable in non-modelled circumstances where the environment cannot be considered as known a priori, a robot assistant must be able to localize and map its environment. The use of a camera sensor to solve localization has several advantages and weaknesses due the nature of the only-bearing data. But as the robot is expected to assist the human agent, this agent can deploy additional sensors and provide the robot with data to help solve the SLAM problem. Thus, another camera worn by the human agent is used to produce non-continuous stereo data with the robotic camera, to speed-up and add robustness to several parts of the monocular SLAM process considered. The approach has been tested with real experiments focused on singular trajectories and other issues found on industrial environments