Experiments on estimating egomotion and structure parameters using long monocular image sequences

This paper presents a simple but robust model based approach to estimating the kinematics of a moving camera and the structure of the objects in a stationary environment using long, noisy, monocular image sequences. Both batch and recursive algorithms are presented and the problem due to occlusion is addressed. The approach is based on representing the constant translational velocity and constant angular velocity of the camera motion using nine rectilinear motion parameters, which are 3-D vectors of the position of the rotation center, linear and angular velocities. The structure parameters are 3-D coordinates of the salient feature points in the inertial coordinate system. Due to redundancies in parameterization, the total number of independent parameters to be estimated is 3M+7, whereM is the number of feature points. The image plane coordinates of these feature points in each frame are first detected and matched over the frames. These noisy image coordinates serve as the input to our algorithms. Due to the nonlinear nature of perspective projection, a nonlinear least squares method is formulated for the batch algorithm, and a conjugate gradient method is then applied to find the solution. A recursive method using an Iterated Extended Kalman Filter (IEKF) for incremental estimation of motion and structure is also presented. Since the plant model is simple in our formulation, closed form solutions for the state and covariance transition equations are easily derived. Experimental results for simulated imagery as well as several real image sequences are included.