Abstract
3D reconstruction of an environment has applications in various fields like city planning, gaming, robotic mapping, virtual environment, motion capture, augmented reality, etc. If spatial data is constructed it can be used for various applications like object tracking, building a path, etc. conventional approaches of accumulating spatial data like physical measurement with visual scrutiny and total survey are manual intensive and protracted. It also provides inaccurate measurements due to lack of ability, knowledge and experience of a labour. An alternative approach to this could be senor technology which can generate 3D point cloud data of an environment with accurate information about presence of object. On the other hand, it’s expensive and implementation of the sensor requires experienced operators. To overcome this challenge Kinect sensors has drawn the attention because of its less cost, ease of use, and higher accessibility with the development of computer vision-based techniques. In this paper, the proposed algorithm is used to extract more features in less time and use random sample consensus (RANSAC) to filter correspondences.
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Srividhya, S., Prakash, S., Elangovan, K. (2020). 3D Reconstruction of an Indoor Environment Using SLAM with Modified SURF and A-KAZE Feature Extraction Algorithm. In: Pandian, A., Ntalianis, K., Palanisamy, R. (eds) Intelligent Computing, Information and Control Systems. ICICCS 2019. Advances in Intelligent Systems and Computing, vol 1039. Springer, Cham. https://doi.org/10.1007/978-3-030-30465-2_16
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