IIIT Hyderabad Publications |
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Frontier Detection and Exploration using Monocular SLAMAuthor: Sarthak Upadhyay Date: 2019-05-30 Report no: IIIT/TH/2019/63 Advisor:Madhava Krishna AbstractIn this thesis, we propose a novel planning technique for monocular camera based Simultaneous Localization and Mapping(VSLAM) and fast Frontier detection method for autonomous explorartion in an indoor evvironment. In VSLAM, the objective is to estimate the trajectory of camera and simultaneously identify key 3D feature points and build a map, using camera as a depth sensor. Unlike a laser range finder(LRF) based SLAM, VSLAM is known to be erroneous when camera motion includes an in-place rotation or feature displacement is large for successive frames. We propose a motion planning framework which combines motion primitives based planning and trajectory optimization approach to generate trajectories which exactly connects an initial and final state and also ensures that the change in camera’s field of view between subsequent instances is less than some specified threshold. As a consequence of this motion planning framework we are able to automate SLAM and generate automated monocular SLAM maps of an indoor lab area. We also show when the robot follows the path of a generic planner, PTAM trajectory breaks more often than when it executes the path computed by the proposed planner. This performance improvement is further utilised to develop an autonomous vision based exploration system. Frontier detection is a critical component in autonomous exploration, wherein the robot decides the next best location to move in order to continue its mapping process. The existing frontier detection methods require dense reconstruction which is difficult to attain in a poorly textured indoor environment using a monocular camera. In this effort, we present an alternate method of detecting frontiers during the course of robot motion that circumvents the requirement of dense mapping. Based on the observation that frontiers typically occur around areas with sudden change in texture (zero-crossings), we propose a novel linear chain Conditional Random Field(CRF) formulation that is able to detect the presence or absence of frontier regions around such areas. We use cues like spread of 3D points and scene change around these areas as an observation to CRF. We demonstrate that this method gives us more relevant frontiers compared to other monocular camera based methods in the literature. Finally, we present results in an indoor environment, wherein frontiers are reliably detected around walls leading to new corridors, doors leading to new rooms or corridors and tables and other objects that open up to a new space in rooms. Full thesis: pdf Centre for Robotics |
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