On the error analysis of vertical line pair-based monocular visual odometry in urban area

Ji, Zhang; Song, Dezhen

doi:10.1109/iros.2009.5354356

Cited by 6 publications

(6 citation statements)

References 16 publications

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“…However, if a feature exists in multiple feature pairs, the problem becomes a convex optimization problem that has to be solved numerically [20]. Here, we directly give the solution for Problem 2, min…”

Section: Translation Recovery By Optimizationmentioning

confidence: 99%

Robust Monocular Visual Odometry for a Ground Vehicle in Undulating Terrain

Zhang

Singh

Kantor

2013

Springer Tracts in Advanced Robotics

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Here we present a robust method for monocular visual odometry capable of accurate position estimation even when operating in undulating terrain. Our algorithm uses a steering model to separately recover rotation and translation. Robot 3DOF orientation is recovered by minimizing image projection error, while, robot translation is recovered by solving an NP-hard optimization problem through an approximation. The decoupled estimation ensures a low computational cost. The proposed method handles undulating terrain by approximating ground patches as locally flat but not necessarily level, and recovers the inclination angle of the local ground in motion estimation. Also, it can automatically detect when the assumption is violated by analysis of the residuals. If the imaged terrain cannot be sufficiently approximated by locally flat patches, wheel odometry is used to provide robust estimation. Our field experiments show a mean relative error of less than 1%.

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Section: Translation Recovery By Optimizationmentioning

confidence: 99%

Robust Monocular Visual Odometry for a Ground Vehicle in Undulating Terrain

Zhang

Singh

Kantor

2013

Springer Tracts in Advanced Robotics

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“…We have developed appearance-based methods [20], investigated how depth error affects navigation [21], studied mirrored surface detection [22], and used vertical line segments for visual odometry tasks [23], [24]. In the process, we have learned that it is necessary to combine the benefits of different features to assist navigation.…”

Section: Related Workmentioning

confidence: 99%

Automatic building exterior mapping using multilayer feature graphs

Song

et al. 2013

2013 IEEE International Conference on Automation Science and Engineering (CASE)

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We develop algorithms that can assist robot to perform building exterior mapping, which is important for building energy retrofitting. In this task, a robot needs to identify building facades in its localization and mapping process, which in turn can be used to assist robot navigation. Existing localization and mapping algorithms rely on low level features such as point clouds and line segments and cannot be directly applied to our task. We attack this problem by employing a multiple layer feature graph (MFG), which contains five different features ranging from raw key points to planes and vanishing points in 3D, in an extended Kalman filter (EKF) framework. We analyze how errors are generated and propagated in the MFG construction process, and then apply MFG data as observations for the EKF to map building facades. We have implemented and tested our MFG-EKF method at three different sites. Experimental results show that building facades are successfully constructed in modern urban environments with mean relative errors of plane depth less than 4.66%.

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“…We have developed appearancebased method [35], investigated how depth error affects navigation [36], and used vertical line segments for visual odometry tasks [37], [38]. In the process, we have realized it is necessary to combine benefits of different features to assist navigation which leads to this work.…”

Section: Related Workmentioning

confidence: 99%

A two-view based multilayer feature graph for robot navigation

Song

et al. 2012

2012 IEEE International Conference on Robotics and Automation

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Abstract-To facilitate scene understanding and robot navigation in a modern urban area, we design a multilayer feature graph (MFG) based on two views from an on-board camera. The nodes of an MFG are features such as scale invariant feature transformation (SIFT) feature points, line segments, lines, and planes while edges of the MFG represent different geometric relationships such as adjacency, parallelism, collinearity, and coplanarity. MFG also connects the features in two views and the corresponding 3D coordinate system. Building on SIFT feature points and line segments, MFG is constructed using feature fusion which incrementally, iteratively, and extensively verifies the aforementioned geometric relationships using random sample consensus (RANSAC) framework. Physical experiments show that MFG can be successfully constructed in urban area and the construction method is demonstrated to be very robust in identifying feature correspondence.I. INTRODUCTION When a mobile robot travels in a modern urban environment, the robot often needs visual signals from its onboard camera to assist navigation. A typical modern urban environment is usually rectilinear and consists of many structured objects and distinctive features such as vertical walls, parallel edges, orthogonal planes, etc. Extracting such features from video frames to form a quick scene understanding can directly benefit navigation tasks such as localization, mapping, obstacle avoidance, and motion planning.Here we design a multilayer feature graph (MFG) to facilitate the scene understanding in urban area. Nodes of an MFG are features such as scale invariant feature transformation (SIFT) feature points, line segments, lines, and planes while edges of the MFG represent different geometric relationships such as adjacency, parallelism, collinearity, and coplanarity. MFG also connects the features in two views and the corresponding 3D world coordinate system. Fig. 1 illustrates the MFG in the 3D world coordinate system. We design an MFG construction method using a feature fusion process which incrementally, iteratively, and extensively verifies the aforementioned geometric relationships using random sample consensus (RANSAC) framework.We have implemented MFG construction algorithm and tested it in physical experiments. Results show that MFG can be successfully constructed from raw image data. Since the process utilizes multiple types of geometric relationships,

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On the error analysis of vertical line pair-based monocular visual odometry in urban area

Cited by 6 publications

References 16 publications

Robust Monocular Visual Odometry for a Ground Vehicle in Undulating Terrain

Robust Monocular Visual Odometry for a Ground Vehicle in Undulating Terrain

Automatic building exterior mapping using multilayer feature graphs

A two-view based multilayer feature graph for robot navigation

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