The EKF follows a predict/update cycle. If no measurement is received within the sensor_timeout period, the filter will carry out a prediction step. If no sensor is available to correct it, the error will grow rapidly, and I wouldn't be surprised to see your pose drift. Is there a reason why your wheel odometry can't publish messages when standing still? I would make it publish messages with decreased covariance values, as you can be certain the robot isn't moving. Also, I'd switch your odometry source so that it is fusing velocity and not pose data.
The EKF follows a predict/update cycle. If no measurement is received within the sensor_timeout period, the filter will carry out a prediction step. If no sensor is available to correct it, the error will grow rapidly, and I wouldn't be surprised to see your pose drift. Is there a reason why your wheel odometry can't publish messages when standing still? I would make it publish messages with decreased covariance values, as you can be certain the robot isn't moving. Also, I'd switch your odometry source so that it is fusing velocity and not pose data.