International Conference on Mining Intelligence and Knowledge Exploration

Mining Intelligence and Knowledge Exploration pp 29-36 | Cite as

New Feature Detection Mechanism for Extended Kalman Filter Based Monocular SLAM with 1-Point RANSAC

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9468)

Abstract

We present a different approach of feature point detection for improving the accuracy of SLAM using single, monocular camera. Traditionally, Harris Corner detection, SURF or FAST corner detectors are used for finding feature points of interest in the image. We replace this with another approach, which involves building non-linear scale space representation of images using Perona and Malik Diffusion equation and computing the scale normalized Hessian at multiple scale levels (KAZE feature). The feature points so detected are used to estimate the state and pose of a mono camera using extended Kalman filter. By using accelerated KAZE features and a more rigorous feature rejection routine combined with 1-point RANSAC for outlier rejection, short baseline matching of features are significantly improved, even with lesser number of feature points, especially in the presence of motion blur. We present a comparative study of our proposal with FAST and show improved localization accuracy in terms of absolute trajectory error.

Keywords

EKF MonoSLAM AKAZE Localization 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Kritikal Solutions Pvt. Ltd.BangaloreIndia

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