Search

In our previous study, we proposed a localization method based on moving horizon estimation (MHE) for known environments. However, in the method the environment map is required for achieving the localization. So in this study, to achieve the localization in unknown environments, we propose a localization method integrating scan-matching and odometry based on MHE; the vehicle pose is estimated by minimizing both errors of scan-matching and odometry, simultaneously. The localization is achieved even in the environment that the pose cannot be localized uniquely by only the scan-matching method since the information for the estimation is complemented by the odometry. We verify the effectiveness of the proposed method through the off-line localizations using the experimental data. The superiority of the proposed method is shown in a comparison with an extended Kalman filter based method.

In this paper, we propose a parking control method with on-line path generations and multiple switching motions using path-following control for front steering vehicles. In the proposed method, the parking control is accomplished by following the reference path which is generated on-line adapting to the surrounding environment. Furthermore, we also realize the multiple switching motions to achieve the parking control in difficult situation. The performance of the proposed method is verified through experiments using a 1/10 scale vehicle. In addition, we show that the feasible area of the parking control is extended comparing with the conventional one.

Localization is an important function for the robots to complete various tasks. For localization, both internal and external sensors are used generally. The odometry is widely used as the method based on the internal sensors, but it suffers from cumulative errors. In the method using the laser range sensor (LRS) which is a kind of external sensor, the estimation accuracy is affected by the number of available measurement data. In our previous study, we applied moving horizon estimation (MHE) to the vehicle localization for integrating the LRS measurement data and the odometry information where the weightings of them are balanced relatively adapting to the number of the available LRS measurement data. In this paper, the effectiveness of the proposed localization method is verified through both numerical simulations and experiments using a 1/10 scale vehicle. The verification is conducted in the situations where the vehicle position cannot be localized uniquely on a certain direction using the LRS measurement data only. We achieve accurate localization even in such a situation by integrating the odometry and LRS based on MHE. We also show the superiority of the method through comparisons with a method using extended Kalman filter (EKF).