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\chapter{Conclusion} \label{chap:conclusion}
\section{Summary}
In this research work, we proposed a state estimation framework based on Extended Kalman Filtering to fuse inertial measurement unit (IMU) data with leg odometry (kinematics of a robot's leg when it is in contact with the ground), in order to estimate the position, orientation, and linear velocity of a free floating base with 6 DoF.\par
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First, we investigated the crucial need for such a framework for legged robots locomotion, and the main reason was the inherent instability of legged robots due to intermittent contact with the ground. In order to have feedback controllers capable of planning robust and efficient motions, the knowledge of robots state at each control step is needed. Some of these states are measured directly with onboard sensors on the robot such as encoders and IMU, but the robot's base placement and linear velocity need external sensing device, or as an alternative method we can utilize efficient sensory data fusion in order to obtain the base states computationally, and completely based on proprioceptive sensing.\par
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Then, we examined the related works on state estimation and elaborated on why the proposed estimation framework was suitable for our research project. In the next step, the mathematical formulation of the state estimator was derived, and the results were implemented on the real hardware.\par
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Finally, we evaluated the performance of the resulting estimator for different motions on the quadruped robot Solo12. The results of these experiments showed that the estimated observable states from our approach are accurate and perfectly matching the ground truth data. Furthermore, we showed that our estimator can be used in a feedback scheme in an observer-based controller with robust behavior for trotting motion, which eliminated the need for any external sensing in order to control the robot locomotion.
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\section{Future Work}
As shown in section~\ref{sec:observer}, the base position and yaw angle are unobservable with the proposed estimation framework. In the future work, we are interested in mounting a stereo camera on the robot, in order to compensate the drift for unobservable states in the EKF. The visual information from the camera will be integrated in the EKF as another measurement model. The advantage of this estimator is that the task of local stabilization and global navigation is done simultaneously.\par
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All of the above works were based on the assumption of a stationary point of contact. This assumption is violated every time there are slippages or deformations of the leg and/or the ground. Contact detection methods can help to reject sporadic slippage or deformation events. However, when these occur regularly, they need to be modeled. As an alternative, learning-based methods can be applied to contact classification problem, in order to make the contact estimation for each leg more robust and accurate.
