arXiv subjects

Guoquan Huang

Publications and source records attributed to Guoquan Huang.

40 records · Page 3Linked to original sources

arXiv2018

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

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 EKF-based visual-inertial SLAM and visual-inertial odometry (VIO) using heterogeneous geometric features in Monte Carlo simulations.

math.OC↗

arXiv2018

Robocentric Visual-Inertial Odometry

In this paper, we propose a novel robocentric formulation of the visual-inertial navigation system (VINS) within a sliding-window filtering framework and design an efficient, lightweight, robocentric visual-inertial odometry (R-VIO) algorithm for consistent motion tracking even in challenging environments using only a monocular camera and a 6-axis IMU. The key idea is to deliberately reformulate the VINS with respect to a moving local frame, rather than a fixed global frame of reference as in the standard world-centric VINS, in order to obtain relative motion estimates of higher accuracy for updating global poses. As an immediate advantage of this robocentric formulation, the proposed R-VIO can start from an arbitrary pose, without the need to align the initial orientation with the global gravitational direction. More importantly, we analytically show that the linearized robocentric VINS does not undergo the observability mismatch issue as in the standard world-centric counterpart which was identified in the literature as the main cause of estimation inconsistency. Additionally, we investigate in-depth the special motions that degrade the performance in the world-centric formulation and show that such degenerate cases can be easily compensated in the proposed robocentric formulation, without resorting to additional sensors as in the world-centric formulation, thus leading to better robustness. The proposed R-VIO algorithm has been extensively tested through both Monte Carlo simulations and real-world experiments with different sensor platforms navigating in different environments, and shown to achieve better (or competitive at least) performance than the state-of-the-art VINS, in terms of consistency, accuracy and efficiency.

cs.RO↗

arXiv2017

Robust Visual SLAM with Point and Line Features

In this paper, we develop a robust efficient visual SLAM system that utilizes heterogeneous point and line features. By leveraging ORB-SLAM [1], the proposed system consists of stereo matching, frame tracking, local mapping, loop detection, and bundle adjustment of both point and line features. In particular, as the main theoretical contributions of this paper, we, for the first time, employ the orthonormal representation as the minimal parameterization to model line features along with point features in visual SLAM and analytically derive the Jacobians of the re-projection errors with respect to the line parameters, which significantly improves the SLAM solution. The proposed SLAM has been extensively tested in both synthetic and real-world experiments whose results demonstrate that the proposed system outperforms the state-of-the-art methods in various scenarios.

cs.CV↗

arXiv2017

Sparse Optimization for Robust and Efficient Loop Closing

It is essential for a robot to be able to detect revisits or loop closures for long-term visual navigation.A key insight explored in this work is that the loop-closing event inherently occurs sparsely, that is, the image currently being taken matches with only a small subset (if any) of previous images. Based on this observation, we formulate the problem of loop-closure detection as a sparse, convex $\ell_1$-minimization problem. By leveraging fast convex optimization techniques, we are able to efficiently find loop closures, thus enabling real-time robot navigation. This novel formulation requires no offline dictionary learning, as required by most existing approaches, and thus allows online incremental operation. Our approach ensures a unique hypothesis by choosing only a single globally optimal match when making a loop-closure decision. Furthermore, the proposed formulation enjoys a flexible representation with no restriction imposed on how images should be represented, while requiring only that the representations are "close" to each other when the corresponding images are visually similar. The proposed algorithm is validated extensively using real-world datasets.

cs.RO↗