Linear vs. non linear minimization in stereo visual odometry

Visual odometry has been an important research activity in the last two years and it has lead to numerous papers being published. Few surveys and comparative studies between approaches exist at the moment. This paper makes a comparative study between two visual odometry methods from accuracy, speed, sensitiveness to noise, and degree of parallelization points of view. The comparison is performed strictly from the perspective of minimizing the cost function, since this is one of the most critical steps in motion estimation from visual data. We also proposed a method based on Kalman filtering to achieve better accuracy in the presence of illumination changes based on correlating the measurement model noise with the intensity variations in time.