Large-scale cooperative 3D visual-inertial mapping in a Manhattan world

Guo, Chao; Sartipi, Kourosh; DuToit, Ryan C.; Γεωργίου, Γεώργιος; Li, Ruipeng; O’Leary, John; Nerurkar, Esha D.; Hesch, Joel A.; Roumeliotis, Stergios I.

doi:10.1109/icra.2016.7487238

Cited by 22 publications

(21 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• Distributed cooperative VINS: Although cooperative VINS have been preliminarily studied in [126,173], it is still challenging to develop real-time distributed VINS, e.g., for crowd sourcing operations. Recent work on cooperative mapping [174,175] may shed some light on how to tackle this problem.…”

Section: Discussionmentioning

confidence: 99%

Visual-Inertial Navigation: A Concise Review

Huang

2019

2019 International Conference on Robotics and Automation (ICRA)

278

119

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Visual-Inertial Navigation: A Concise Review

Huang

2019

2019 International Conference on Robotics and Automation (ICRA)

278

119

View full text Add to dashboard Cite

show abstract

“…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%

“…Stacking (29), (32) and (33) yields the complete the measurement Jacobian of the plane measurement w.r.t. the state (1):…”

Section: Closest Point (Cp) Parameterizationmentioning

confidence: 99%

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

Yang

Huang

2019

IEEE Trans. Robot.

View full text Add to dashboard Cite

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...

show abstract

“…With our system state and measurement models defined, in what follows, we present how measurements (4), (6), and (8) are processed in a consistent manner.…”

Section: Mapped-feature Measurement Modelmentioning

confidence: 99%

“…Additionally, and in order to reduce the processing requirements of the C-SKF -between linear and quadratic in the map's size -we introduce a consistent relaxation of the C-SKF, the sub-map (s)C-SKF, which trades localization accuracy for processing speed by operating on the Cholesky factors of the partitioned Hessians resulting from dividing the original map into independent sub-maps. Note that the sub-maps used throughout this work are generated from the method of [8], however, other methods that produce submaps (i.e., [5]) could be employed as well. This approximation allows mapping larger areas and/or operating on resourceconstrained mobile devices, such as cell phones and tablets.…”

Section: Introductionmentioning

confidence: 99%

Consistent map-based 3D localization on mobile devices

DuToit

Hesch

Nerurkar

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

Self Cite

View full text Add to dashboard Cite

The objective of this paper is to provide consistent, real-time 3D localization capabilities to mobile devices navigating within previously mapped areas. To this end, we introduce the Cholesky-Schmidt-Kalman filter (C-SKF), which explicitly considers the uncertainty of the prior map, by employing the sparse Cholesky factor of the map's Hessian, instead of its dense covariance-as is the case for the Schmidt-Kalman filter (SKF). By doing so, the C-SKF has memory requirements typically linear in the size of the map, as opposed to quadratic for storing the map's covariance. Moreover, and in order to bound the processing needs of the C-SKF (between linear and quadratic in the size of the map), we introduce a relaxation of the C-SKF algorithm, the sC-SKF, which operates on the Cholesky factors of independent submaps resulting from dividing the trajectory and observations used for constructing the map into overlapping segments. Lastly, we assess the processing and memory requirements of the proposed C-SKF and sC-SKF algorithms, and compare their positioning accuracy against other approximate map-based localization approaches that employ measurement-noise-covariance inflation to compensate for the map's uncertainty.• We introduce the Cholesky-Schmidt-Kalman filter (C-SKF), which employs the Hessian's Cholesky factor to compactly represent the map's uncertainty, and efficiently compute consistent map-based updates.• We introduce the sub-map (s)C-SKF, a relaxation of the C-SKF, which employs multiple, independent sub-maps

show abstract

Large-scale cooperative 3D visual-inertial mapping in a Manhattan world

Cited by 22 publications

References 28 publications

Visual-Inertial Navigation: A Concise Review

Visual-Inertial Navigation: A Concise Review

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

Consistent map-based 3D localization on mobile devices

Contact Info

Product

Resources

About