Trifo-VIO: Robust and Efficient Stereo Visual Inertial Odometry Using Points and Lines

Zheng, Feng; Tsai, Grace; Zhang, Zhe; Liu, Shaoshan; Chu, Chen-Chi; Hu, Hsiao Yun

doi:10.1109/iros.2018.8594354

Cited by 50 publications

(36 citation statements)

References 31 publications

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“…The (right) invariant Kalman filter [56] was recently employed to improve filter consistency [25,57,58,59,60], as well as the (iterated) EKF that was also used for VINS in robocentric formulations [22,61,62,63]. On the other hand, in the EKF framework, different geometric features besides points have also been exploited to improve VINS performance, for example, line features used in [64,65,66,67,68] and plane features in [69,70,71,72]. In addition, the MSCKF-based VINS was also extended to use rolling-shutter cameras with inaccurate time synchronization [64,73], RGBD cameras [69,74], multiple cameras [53,75,76] and multiple IMUs [77].…”

Section: Filtering-based Vs Optimization-based Estimationmentioning

confidence: 99%

“…DuToit et al [102] exploited the idea of Schmidt KF [111] and developed a Cholesky-Schmidt EKF, which, however, uses a prior map with its full uncertainty and relaxes all the correlations between the mapped features and the current state variables; while our latest Schmidt-MSCKF [86] integrates loop closures in a single thread. Moreover, the recent point-line VIO [67] treats the 3D positions of marginalized keypoints as "true" for loop closure, which may lead to inconsistency.…”

Section: Vio Vs Slammentioning

confidence: 99%

See 1 more Smart Citation

Visual-Inertial Navigation: A Concise Review

Huang

2019

2019 International Conference on Robotics and Automation (ICRA)

278

119

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As inertial and visual sensors are becoming ubiquitous, visual-inertial navigation systems (VINS) have prevailed in a wide range of applications from mobile augmented reality to aerial navigation to autonomous driving, in part because of the complementary sensing capabilities and the decreasing costs and size of the sensors. In this paper, we survey thoroughly the research efforts taken in this field and strive to provide a concise but complete review of the related work -which is unfortunately missing in the literature while being greatly demanded by researchers and engineers -in the hope to accelerate the VINS research and beyond in our society as a whole.where δq describes the small rotation that causes the true and estimated attitude to coincide. The advantage of this parametrization permits a minimal representation, 3 × 3 covariance matrix E I θ I θ T , for the attitude uncertainty.

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Section: Filtering-based Vs Optimization-based Estimationmentioning

confidence: 99%

Section: Vio Vs Slammentioning

confidence: 99%

Visual-Inertial Navigation: A Concise Review

Huang

2019

2019 International Conference on Robotics and Automation (ICRA)

278

119

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“…Ref. [12] developed a tightly-coupled visual inertial odometry (VIO) using both point and line segments based on extended Kalman filter (EKF) framework. The above works parametrize the 3D lines by its two endpoints, which bring difficulty to handle partial occlusion and viewpoint variance.…”

Section: Related Workmentioning

confidence: 99%

A robust visual SLAM system in dynamic man-made environments

Liu

Meng

You

2020

Sci. China Technol. Sci.

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This paper presents a robust visual simultaneous localization and mapping (SLAM) system that leverages point and structural line features in dynamic man-made environments. Manhanttan world assumption is considered and the structural line features in such man-made environments provide rich geometric constraint, e.g., parallelism. Such a geometric constraint can be therefore used to rectify 3D maplines after initialization. To cope with dynamic scenarios, the proposed system are divided into four main threads including 2D dynamic object tracking, visual odometry, local mapping and loop closing. The 2D tracker is responsible to track the object and capture the moving object in bounding boxes. In such a case, the dynamic background can be excluded and the outlier point and line features can be effectively removed. To parameterize 3D lines, we use Plücker line coordinates in initialization and projection processes, and utilize the orthonormal representation in unconstrained graph optimization process. The proposed system has been evaluated in both benchmark datasets and real-world scenarios, which reveals a more robust performance in most of the experiments compared with the existing state-of-the-art methods.

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“…As mentioned earlier, vision-aided INS (VINS) arguably is among the most popular localization methods in particular for resource-constrained sensor platforms such as mobile devices and micro aerial vehicles (MAVs) navigating in GPS-denied environments (e.g., see [26,27,10,28]). While most current VINS algorithms focus on using point features (e.g., [7,8,9,10]), line and plane features may not be blindly discarded in structured environments [29,30,31,32,33,34,35,36,24], in part because: (i) they are ubiquitous and compact in many urban or indoor environments (e.g., doors, walls, and stairs), (ii) they can be detected and tracked over a relatively long time period, and (iii) they are more robust in texture-less environments compared to point features.…”

Section: Aided Ins With Points Lines and Planesmentioning

confidence: 99%

Observability Analysis of Aided INS With Heterogeneous Features of Points, Lines, and Planes

Yang

Huang

2019

IEEE Trans. Robot.

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In this paper, we perform a thorough observability analysis for linearized inertial navigation systems (INS) aided by exteroceptive range and/or bearing sensors (such as cameras, LiDAR and sonars) with different geometric features (points, lines and planes). While the observability of vision-aided INS (VINS) with point features has been extensively studied in the literature, we analytically show that the general aided INS with point features preserves the same observability property -that is, 4 unobservable directions, corresponding to the global yaw and the global position of the sensor platform. We further prove that there are at least 5 (and 7) unobservable directions for the linearized aided INS with a single line (and plane) feature; and, for the first time, analytically derive the unobservable subspace for the case of multiple lines/planes. Building upon this, we examine the system observability of the linearized aided INS with different combinations of points, lines and planes, and show that, in general, the system preserves at least 4 unobservable directions, while if global measurements are available, as expected, some unobservable directions diminish. In particular, when using plane features, we propose to use a minimal, closest point (CP) representation; and we also study in-depth the effects of 5 degenerate motions identified on observability. To numerically validate our analysis, we develop and evaluate both EKFbased visual-inertial SLAM and visual-inertial odometry (VIO) using heterogeneous geometric features in Monte Carlo simulations. Related WorkAided INS is a classical research topic with significant body of literature [25] and has recently been reemerging in part due to the advancement of sensing and computing technologies. In this section, we briefly review the related literature closest to this work by focusing on the vision-aided INS. Aided INS with Points, Lines, and PlanesAs mentioned earlier, vision-aided INS (VINS) arguably is among the most popular localization methods in particular for resource-constrained sensor platforms such as mobile devices and micro aerial vehicles (MAVs) navigating in GPS-denied environments (e.g., see [26,27,10,28]). While most current VINS algorithms focus on using point features (e.g., [7,8,9,10]), line and plane features may not be blindly discarded in structured environments [29,30,31,32,33,34,35,36,24], in part because: (i) they are ubiquitous and compact in many urban or indoor environments (e.g., doors, walls, and stairs), (ii) they can be detected and tracked over a relatively long time period, and (iii) they are more robust in texture-less environments compared to point features.In the case of utilizing line features, Kottas et al. [29] represented the line with a quaternion and a distance scalar and studied the observability properties for linearized VINS with this line parameterization. Yu et al. [30] proposed a minimal four-parameter representation of line features for VIO using rolling-shutter cameras, while Zheng et al. [31] used two end points t...

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Trifo-VIO: Robust and Efficient Stereo Visual Inertial Odometry Using Points and Lines

Cited by 50 publications

References 31 publications

Visual-Inertial Navigation: A Concise Review

Visual-Inertial Navigation: A Concise Review

A robust visual SLAM system in dynamic man-made environments

Observability Analysis of Aided INS With Heterogeneous Features of Points, Lines, and Planes

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