Monocular SLAM with undelayed initialization for an indoor robot

This paper presents a new feature initialization method for monocular EKF SLAM (Extended Kalman Filter Simultaneous Localization and Mapping) which utilizes a 3D measurement model in the camera frame rather than 2D pixel coordinates in the image plane. The key idea is to regard a camera as a range and bearing sensor, of which the range information contains numerous uncertainties. 2D pixel coordinates of measurement are converted to 3D points in the camera frame with an assumed depth. The element of the measurement noise covariance corresponding to the depth of the feature is set to a very high value. And it is shown that the proposed measurement model has very little linearization error, which can be critical for the EKF performance. Furthermore, this paper proposes an EKF SLAM system that combines odometry, a low-cost gyro, and low frame rate (1-2 Hz) monocular vision. Low frame rate is crucial for reducing the price of the processor. This system combination is cost-effective enough to be commercialized for a real vacuum cleaning application. Simulations and experimental results show the efficacy of the proposed method with computational efficiency in indoor environments.