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Gokul B. Nair – Dual Degree CSE

Gokul B. Nair received his MS-Dual Degree in Computer Science and Engineering. His  research work was supervised by Prof. Madhava Krishna.   Here’s a summary of Gokul B. Nair’s M.S thesis, Monocular Multibody SLAM in Dynamic Environments as explained by him:

This thesis proposes a novel pose-graph based formulation to map moving object trajectories onto a stationary global reference frame where the moving objects are observed from a single moving camera. This is performed in a multi-body fashion as a consequence of which we obtain accurate estimations to both the ego-camera trajectories as well as trajectories to multiple dynamic participants in the scene. This set up is dovetailed to on-road dynamic vehicles observed from a monocular sensor mounted on a moving ego-car such as in the KITTI dataset. The original triangulation problem is intractable in this scenario as it is impossible to triangulate a moving object from a single moving camera unambiguously unless there are appropriate restrictions on the object motion. The problem of unobservability also manifests in the form of the relative scale problem in classical multi-body SFM/SLAM formulations wherein there exists an unresolved scale factor (called as relative scale) such that a family of infinitely many solutions exist for any pair of motions in the scene. This prevents accurately representing the moving vehicle and camera in the same stationary frame. We overcome this relative scale factor by leveraging single-view metrology, advances in deep learning, and category-level shape estimation. We show in this paper by invoking single view reconstruction techniques, that moving objects can be represented in the same global frame in which the camera trajectory is represented without any assumptions or restrictions on object motion. More specifically we solve for the relative scale problem through a factor graph formulation where the nodes include camera and moving object poses and thereby obtain trajectories of the moving object in the starting camera frame. We use Gaussian Process based motion model prediction and lane constraints to further improve the trajectory estimates and show performance gain with previous formulations that have attacked this problem. This optimization helps us reduce the average error in trajectories of multiple bodies over real-world datasets, such as KITTI. To the best of our knowledge, our method is the first practical monocular multi-body SLAM system to perform dynamic multi-object and ego localization in a unified framework in metric scale.