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.
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