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Image based reconstruction of urban environments is a challenging problemthat deals with optimization of large number of variables, and has severalsources of errors like the presence of dynamic objects. Since most large scaleapproaches make the assumption of observing static scenes, dynamic objects arerelegated to the noise modeling section of such systems. This is an approach ofconvenience since the RANSAC based framework used to compute most multiviewgeometric quantities for static scenes naturally confine dynamic objects to theclass of outlier measurements. However, reconstructing dynamic objects alongwith the static environment helps us get a complete picture of an urbanenvironment. Such understanding can then be used for important robotic taskslike path planning for autonomous navigation, obstacle tracking and avoidance,and other areas. In this paper, we propose a system for robust SLAM that worksin both static and dynamic environments. To overcome the challenge of dynamicobjects in the scene, we propose a new model to incorporate semanticconstraints into the reconstruction algorithm. While some of these constraintsare based on multi-layered dense CRFs trained over appearance as well as motioncues, other proposed constraints can be expressed as additional terms in thebundle adjustment optimization process that does iterative refinement of 3Dstructure and camera / object motion trajectories. We show results on thechallenging KITTI urban dataset for accuracy of motion segmentation andreconstruction of the trajectory and shape of moving objects relative to groundtruth. We are able to show average relative error reduction by a significantamount for moving object trajectory reconstruction relative to state-of-the-artmethods like VISO 2, as well as standard bundle adjustment algorithms.

Dynamic Body VSLAM with Semantic Constraints