Monocular Visual Odometry Method Research Articles

Geometry-Constrained Scale Estimation for Monocular Visual Odometry

We propose a robust geometry-constrained scale estimation approach for monocular visual odometry, which takes the camera height as an absolute reference. Visual odometry is an essential module for robot self-localization and autonomous navigation in unexplored environments. Scale recovery is an indispensable requirement for monocular visual odometry, since it compensates for the metric information lost by a single camera and helps to reduce the scale drift. When the camera height is considered the absolute reference, the precision of scale recovery depends on the accuracy of the road point selection and road geometric model calculation. However, most of the previous approaches solve these two problems sequentially, and their road point selection is based on the color model of the road or prior-knowledge-based fixed region. In this paper, we propose combining and iteratively solving these two problems. We adopt the geometric model, instead of the color model, of the road to select the road points. Furthermore, the selected road feature points are used to estimate the road model, which limits the road point selection. In detail, we segment our feature points with Delaunay triangulation and select road points based on the depth consistency and road model consistency. The experiments on the KITTI dataset show that our method achieves the best performance among state-of-the-art monocular visual odometry methods.

A Monocular Visual Odometry Method Based on Virtual-Real Hybrid Map in Low-Texture Outdoor Environment.

With the extensive application of robots, such as unmanned aerial vehicle (UAV) in exploring unknown environments, visual odometry (VO) algorithms have played an increasingly important role. The environments are diverse, not always textured, or low-textured with insufficient features, making them challenging for mainstream VO. However, for low-texture environment, due to the structural characteristics of man-made scene, the lines are usually abundant. In this paper, we propose a virtual-real hybrid map based monocular visual odometry algorithm. The core idea is that we reprocess line segment features to generate the virtual intersection matching points, which can be used to build the virtual map. Introducing virtual map can improve the stability of the visual odometry algorithm in low-texture environment. Specifically, we first combine unparallel matched line segments to generate virtual intersection matching points, then, based on the virtual intersection matching points, we triangulate to get a virtual map, combined with the real map built upon the ordinary point features to form a virtual-real hybrid 3D map. Finally, using the hybrid map, the continuous camera pose estimation can be solved. Extensive experimental results have demonstrated the robustness and effectiveness of the proposed method in various low-texture scenes.

Direct Visual Odometry Using Lines for a Monocular Camera

Most of the visual odometry is based on the matching of the feature points, or the pixel matching of the direct method. However, images have another obvious feature, i.e. the line feature. If we use the point based visual odometry in low texture images, it may result in bad performance in the experiment because of few numbers of feature points. Although in some texture-less environments, it is still possible to reliably estimate the line based on geometric elements. For example, the structured edges are obvious in the indoor scenes. In this paper, we propose a monocular visual odometry method which is based on the combination of direct method and line feature. Then we use TUM-RGB and ICL-NUIM datasets to test our algorithm. Experimental results show that our method improves the robustness and accuracy of the estimation of the position and attitude of the camera.

Hybrid-Residual-Based RGBD Visual Odometry

Visual odometry has greatly progressed since non-linear optimization methods were introduced for pose estimation. Furthermore, RGBD visual odometry has become a hot research topic in the robotic and computer vision field with the introduction of RGBD cameras. However, most RGBD-camera-based visual odometry methods are designed by extending monocular visual odometry methods, therein not paying much attention to the integration of the different types of information provided by RGBD images. In this paper, we propose a novel hybrid-residual-based-RGBD visual odometry, where three types of complementary information are integrated into a joint optimization model. The reprojection residuals, the photometric residuals and the depth residuals are minimized together in the non-linear optimization process, where a robust cost function and outlier filtering are employed in the iterative optimization to enhance the robustness of the iteration while simultaneously maintaining the optimality. Experiments on publicly available RGBD data sets validate the advantages of the integration of multiple types of information for RGBD visual odometry. The accuracy and robustness are greatly improved by our method.

Robust monocular visual odometry for road vehicles using uncertain perspective projection

Many emerging applications in the field of assisted and autonomous driving rely on accurate position information. Satellite-based positioning is not always sufficiently reliable and accurate for these tasks. Visual odometry can provide a solution to some of these shortcomings. Current systems mainly focus on the use of stereo cameras, which are impractical for large-scale application in consumer vehicles due to their reliance on accurate calibration. Existing monocular solutions on the other hand have significantly lower accuracy. In this paper, we present a novel monocular visual odometry method based on the robust tracking of features in the ground plane. The key concepts behind the method are the modeling of the uncertainty associated with the inverse perspective projection of image features and a parameter space voting scheme to find a consensus on the vehicle state among tracked features. Our approach differs from traditional visual odometry methods by applying 2D scene and motion constraints at the lowest level instead of solving for the 3D pose change. Evaluation both on the public KITTI benchmark and our own dataset show that this is a viable approach for visual odometry which outperforms basic 3D pose estimation due to the exploitation of the largely planar structure of road environments.

Incorporating a wheeled vehicle model in a new monocular visual odometry algorithm for dynamic outdoor environments.

This paper presents a monocular visual odometry algorithm that incorporates a wheeled vehicle model for ground vehicles. The main innovation of this algorithm is to use the single-track bicycle model to interpret the relationship between the yaw rate and side slip angle, which are the two most important parameters that describe the motion of a wheeled vehicle. Additionally, the pitch angle is also considered since the planar-motion hypothesis often fails due to the dynamic characteristics of wheel suspensions and tires in real-world environments. Linearization is used to calculate a closed-form solution of the motion parameters that works as a hypothesis generator in a RAndom SAmple Consensus (RANSAC) scheme to reduce the complexity in solving equations involving trigonometric. All inliers found are used to refine the winner solution through minimizing the reprojection error. Finally, the algorithm is applied to real-time on-board visual localization applications. Its performance is evaluated by comparing against the state-of-the-art monocular visual odometry methods using both synthetic data and publicly available datasets over several kilometers in dynamic outdoor environments.

Error Aware Monocular Visual Odometry using Vertical Line Pairs for Small Robots in Urban Areas

We report a new error-aware monocular visual odometry method that only uses vertical lines, such as vertical edges of buildings and poles in urban areas as landmarks. Since vertical lines are easy to extract, insensitive to lighting conditions/ shadows, and sensitive to robot movements on the ground plane, they are robust features if compared with regular point features or line features. We derive a recursive visual odometry method based on the vertical line pairs. We analyze how errors are propagated and introduced in the continuous odometry process by deriving the closed form representation of covariance matrix. We formulate the minimum variance ego-motion estimation problem and present a method that outputs weights for different vertical line pairs. The resulting visual odometry method is tested in physical experiments and compared with two existing methods that are based on point features and line features, respectively. The experiment results show that our method outperforms its two counterparts in robustness, accuracy, and speed. The relative errors of our method are less than 2% in experiments.