Optimization Algorithm Toward Deep Features Based Camera Pose Estimation

Deep convolutional neural networks are proved to be end-to-end localization method which tolerates large baselines. However, it relies on the training data and furthermore, camera pose estimation results are not robustness and smooth enough. It all boils down to without back-end optimization (e.g. local bundle adjustment). This paper proposes a deep features based SLAM optimization method as well as improves the pose estimation precision by the constraint function. The contribution of our paper is two-fold: (1) We present constraint function based on similarity for fast 2D-3D points mapping and a new optimization approach that estimates camera exterior parameter using multiple feature fusion. (2) For the problem of instability in Cnn based SLAM, a multiple features ensemble bundle adjustment optimization algorithm is presented. Most existing localization approaches simply approximate pose confidence based on reference point distance. Unlike previous work, we employ reconstruction data as a reference, then, the visible 3D points and its related key-points from off-line data sets by random forests are mapped, and a multiple feature fusion is used to measure the assessment score by an constraint function. The above method is used to optimize deep features based SLAM. Experimental results demonstrate the robustness analysis of our algorithm in handling various challenges for estimation of camera pose.