Mobile Robot Localization Using a Gyroscope and Constrained Kalman Filter

Recently, cleaning robots are gaining popularity for saving time and household labor. To clean the room effectively, the robot should have localization capability. The odometry information used in low-cost localization can be quite erroneous when the robot suffers from slippage. Thus the use of the low-cost MEMS gyroscope to compensate for an angular error is considered by many researchers. Conventional Kalman filtering method that fuses the odometry with the gyroscope may produce infeasible solution because the robot parameters are estimated regardless of their physical constraints. In this paper, we propose a constrained Kalman filtering method that applies general constrained optimization technique to the estimation of the robot parameters. The state observability is improved by the additional state variables and the accuracy is also improved by the non-approximated Kalman filter design. Experimental results show the proposed method effectively compensates for the odometry error and yields feasible parameter estimation at the same time