Towards New Sensing Capabilities For Legged Locomotion Using Real-Time State Estimation With Low-Cost IMUs

Estimation in robotics is an important subject affected by trade-offs between some major critera from which we can cite the computation time and the accuracy. The importance of these two criteria are application-dependent. If the computation time is not important for off-line methods, it becomes critical when the application has to run on real-time. Similarly, accuracy requirements are dependant on the applications. EKF estimators are widely used to satisfy real-time constraints while achieving acceptable accuracies. One sensor widely used in trajectory estimation problems remains the inertial measurement units (IMUs) providing data at a high rate. The main contribution of this thesis is a clear presentation of the preintegration theory yielding in a better use IMUs. We apply this method for estimation problems in both pedestrian and humanoid robots navigation to show that real-time estimation using a low-cost IMU is possible with smoothing methods while formulating the problems with a factor graph. We also investigate the calibration of the IMUs as it is a critical part of those sensors. All the development made during this thesis was thought with a visual-inertial SLAM background as a mid-term perspective. Furthermore, this work tries to rise another question when it comes to legged robots. In opposition to their usual architecture, could we use multiple low-cost IMUs on the robot to get valuable information about the motion being executed?