This paper presents a novel approach for optimising visual odometry results in a dynamic outdoor environment. Egomotion estimation is still considered to be one of the more difficult tasks in computer vision because of its continued computation pipeline: every phase of visual odometry can be a source of noise or errors, and influence future results. Also, tracking features in a dynamic environment is very challenging. Since feature tracking can only match two features in integer coordinates, there will be a data loss at sub-pixel level. In this paper we introduce a weighting scheme that measures the geometric relations between different layers: We divide tracked features into three groups based on geometric constrains; each group is recognised as being a "layer". Each layer has a weight which depends on the distribution of the grouped features on the 2D image and the actual position in 3D scene coordinates. This geometric multi-layer approach can effectively remove all the dynamic features in the scene, and provide more reliable feature tracking results. Moreover, we propose a 3-state Kalman filter optimisation approach. Our method follows the traditional process of visual odometry algorithms by focusing on motion estimation between pairs of two consecutive frames. Experiments and evaluations are carried out for trajectory estimation. We use the provided ground truth of the KITTI data-sets to analyse mean rotation and translation errors over distance.
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