This paper presents an odometer architecture which combines a monocular camera and an inertial measurement unit (IMU). The trifocal tensor geometry relationship between three images is used as camera measurement information, which makes the proposed method without estimating the 3D position of feature point. In other words, the proposed method does not have to reconstruct environment. Meanwhile, the camera pose corresponding to each of the three images are refined in filter to form a multi-state constraint Kalman filter (MSCKF). Consequently, this paper proposes a sliding window odometry which has a balance between computational cost and accuracy. Compared with traditional visual odometry or simultaneous localization and mapping (SLAM) method, the proposed method not only meets the requirement of odometer in the ego-motion estimation, but also suit for real-time application. This paper further proposes a random sample consensus (RANSAC) algorithm which is based on three views geometry. The RANSAC algorithm can effectively reject feature points which are mismatch or located on independently moving objects, thus it make the overall algorithm capable of operating in dynamic environment. Experiments are conducted to show the effectiveness of the proposed method in real environment.

In this paper, we present a novel approach for monocular visual odometry combined with an inertial measurement unit to estimate the human walking trajectory. The scale factor is computed from the walking speed estimation derived by the kinematic model of human walking. This model not only keeps the important characteristic in biped rolling-foot model, but also makes the speed estimation feasible using human body acceleration. A wearable and calibrated IMU-Camera device is made to mount on the center of human's waist for experimental verification. With the human walking speed and gyroscope measurement, an Unscented Kalman filter is implemented to estimate the current scale factor and refine the attitude of the camera. Experiments in an indoor environment are conducted to show the effectiveness of the proposed method.