Visual Odometry, Simultaneous Localization and Mapping (SLAM) and 3D reconstruction have become increasingly popular these years with the fast development of robotics and mobile vision applications. Particularly, monocular SLAM only requires an optical camera which is cheap, light, and versatile, and hence can easily be put into commodity system. Generally, feature-based and direct methods are two main approaches of Monocular SLAM. We are particularly interested in applying visual odometry/SLAM algorithm in a highly oscillating environment. We are interested in the performance of both feature based and direct based visual odomoetry/SLAM methods in such an environment. The most cited related works of feature based method include PTAM and ORB-SLAM. Since we are dealing with an oscillating environment that requires robust and wide baseline feature matches, we thus implemented a Feature Based Monocular SLAM system on our own for comparison.
To compare and contrast the feature based method with direct ones. We look into great detail the visual odometry method of ERL , which works based on optical flow weighting and optimisation and adjust it based on the oscillating environment scenario. A MATLAB based visualiser was built for this particular method. Furthermore, a visualisation tool to compare and contrast these two approaches in a uniform manner is built in PCL.
As shown in the above pipeline, the system of Feature Based Monocular SLAM that we implemented from scratch consists of these following steps: map initialization by eight point algorithm and RANSAC, data association refinement by Perspective-n-Point algorithm, mapping by bundle adjustment, and 3D dense reconstruction with depth measurement.
Feature Matching and Epipolar Constriant Example: