MIMU sensors for joint kinematic estimation: anatomical landmarking and frame correction

The ability to capture human motion allows researchers to evaluate an individual’s gait. Gait can be measured in different ways, from camera-based systems to Magnetic and Inertial Measurement Units (MIMU). The former uses cameras to track positional information of photo-reflective markers, while the latter uses accelerometers, gyroscopes, and magnetometers to measure segment orientation. Both systems can be used to measure joint kinematics, but the results vary because of their differences in anatomical calibrations. The objective of this thesis was to study potential solutions for reducing joint angle discrepancies between MIMU and camera-based systems. The first study worked to correct the anatomical frame differences between MIMU and camera-based systems via the joint angles of both systems. This study looked at full lower body correction versus correcting a single joint. Single joint correction showed slightly better alignment of both systems, but does not take into account that body segments are generally affected by more than one joint. The second study explores the possibility of anatomical landmarking using a single camera and a pointer apparatus. Results showed anatomical landmark position could be determined using a single camera, as the anatomical landmarks found from this study and a camera-based system showed similar results. This thesis worked on providing a novel way for obtaining anatomical landmarks with a single point-and-shoot camera, as well aligning anatomical frames between MIMUs and camera-based systems using joint angles.

Towards Mapping of Rock Walls Using a UAV-Mounted 2D Laser Scanner in GPS Denied Environments

In geotechnical engineering, the stability of rock excavations and walls is estimated by using tools that include a map of the orientations of exposed rock faces. However, measuring these orientations by using conventional methods can be time consuming, sometimes dangerous, and is limited to regions of the exposed rock that are reachable by a human. This thesis introduces a 2D, simulated, quadcopter-based rock wall mapping algorithm for GPS denied environments such as underground mines or near high walls on surface. The proposed algorithm employs techniques from the field of robotics known as simultaneous localization and mapping (SLAM) and is a step towards 3D rock wall mapping. Not only are quadcopters agile, but they can hover. This is very useful for confined spaces such as underground or near rock walls. The quadcopter requires sensors to enable self localization and mapping in dark, confined and GPS denied environments. However, these sensors are limited by the quadcopter payload and power restrictions. Because of these restrictions, a light weight 2D laser scanner is proposed. As a first step towards a 3D mapping algorithm, this thesis proposes a simplified scenario in which a simulated 1D laser range finder and 2D IMU are mounted on a quadcopter that is moving on a plane. Because the 1D laser does not provide enough information to map the 2D world from a single measurement, many measurements are combined over the trajectory of the quadcopter. Least Squares Optimization (LSO) is used to optimize the estimated trajectory and rock face for all data collected over the length of a light. Simulation results show that the mapping algorithm developed is a good first step. It shows that by combining measurements over a trajectory, the scanned rock face can be estimated using a lower-dimensional range sensor. A swathing manoeuvre is introduced as a way to promote loop closures within a short time period, thus reducing accumulated error. Some suggestions on how to improve the algorithm are also provided.