Monocular Visual-IMU Odometry: A Comparative Evaluation of the Detector-Descriptor Based Methods
Visual odometry has been used in many fields, especially in robotics and intelligent vehicles. Since local descriptors are robust to background clutter, occlusion and other content variations, they have been receiving more and more attention in the applic
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1 Ocean University of China, Qingdao 266071, China Centre for Imaging Sciences, University of Manchester, Manchester M13 9PT, UK [email protected]
Abstract. Visual odometry has been used in many fields, especially in robotics and intelligent vehicles. Since local descriptors are robust to background clutter, occlusion and other content variations, they have been receiving more and more attention in the application of the detector-descriptor based visual odometry. To our knowledge, however, there is no extensive, comparative evaluation investigating the performance of the detector-descriptor based methods in the scenario of monocular visual-IMU (Inertial Measurement Unit) odometry. In this paper, we therefore perform such an evaluation under a unified framework. We select five typical routes from the challenging KITTI dataset by taking into account the length and shape of routes, the impact of independent motions due to other vehicles and pedestrians. In terms of the five routes, we conduct five different experiments in order to assess the performance of different combinations of salient point detector and local descriptor in various road scenes, respectively. The results obtained in this study potentially provide a series of guidelines for the selection of salient point detectors and local descriptors. Keywords: Monocular visual-IMU odometry Odometry Salient point detectors Local descriptors Evaluation
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1 Introduction Ego-motion estimation in real-world environments has been studied over the past decades. As one of the commonly-used methods for this problem, Visual Odometry (VO) estimates the pose of a vehicle by matching the consecutive images captured using the onboard camera [28]. According to the camera involved, visual odometry can be divided into two categories: monocular and stereo [28]. However, the architecture of stereo visual odometry systems is normally complex, which limits their practical applications. Stereo visual odometry also tends to degenerate to a monocular system when the distance between objects and the camera is large. On the other hand, Electronic supplementary material The online version of this chapter (doi:10.1007/978-3-31946604-0_6) contains supplementary material, which is available to authorized users. © Springer International Publishing Switzerland 2016 G. Hua and H. Jégou (Eds.): ECCV 2016 Workshops, Part I, LNCS 9913, pp. 81–95, 2016. DOI: 10.1007/978-3-319-46604-0_6
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monocular visual odometry systems are simple and can be easily used in practical applications. In addition, the joint use of the Inertial Measure Unit (IMU) and the camera (referred to as Visual-IMU Odometry) normally improves both the reliability and accuracy of motion estimation [19] because they are complementary [3]. Hence, the scope of this research is limited to the study of monocular visual-IMU odometry. Considering local descriptors are insensitive to occlusion, background clutter and other changes [23], they have been extensively applied to visual odometry
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