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Shengze Wang 0002

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3 papers

1 author row

ICRA Conference 2022 Conference Paper

EDPLVO: Efficient Direct Point-Line Visual Odometry

Lipu Zhou
Guoquan Huang 0003
Yinian Mao
Shengze Wang 0002
Michael Kaess

This paper introduces an efficient direct visual odometry (VO) algorithm using points and lines. Pixels on lines are generally adopted in direct methods. However, the original photometric error is only defined for points. It seems difficult to extend it to lines. In previous works, the collinear constraints for points on lines are either ignored [1] or introduce heavy computational load into the resulting optimization system [2]. This paper extends the photometric error for lines. We prove that the 3D points of the points on a 2D line are determined by the inverse depths of the endpoints of the 2D line, and derive a closed-form solution for this problem. This property can significantly reduce the number of variables to speed up the optimization, and can make the collinear constraint exactly satisfied. Furthermore, we introduce a two-step method to further accelerate the optimization, and prove the convergence of this method. The experimental results show that our algorithm outperforms the state-of-the-art direct VO algorithms.

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ICRA Conference 2021 Conference Paper

π-LSAM: LiDAR Smoothing and Mapping With Planes

Lipu Zhou
Shengze Wang 0002
Michael Kaess

This paper introduces a real-time dense planar LiDAR SLAM system, named π-LSAM, for the indoor environment. The widely used LiDAR odometry and mapping (LOAM) framework [1] does not include bundle adjustment (BA) and generates a low fidelity tracking pose. This paper seeks to overcome these drawbacks for the indoor environment. Specifically, we use the plane as the landmark, and introduce plane adjustment (PA) as our back-end to jointly optimize planes and keyframe poses. We present the π-factor to significantly reduce the computational complexity of PA. In addition, we introduce an efficient loop detection algorithm based on the RANSAC framework using planes. In the front-end, our algorithm performs global registration in real time. To achieve this performance, we maintain the local-to-global point-to-plane correspondences scan by scan, so that we only need a small local KD-tree to establish the data association between a LiDAR scan and the global planes, rather than a large global KD-tree used in previous works. With this local-to-global data association, our algorithm directly identifies planes in a LiDAR scan, and yields an accurate and globally consistent pose. Experimental results show that our algorithm significantly outperforms the state-of-the-art LOAM variant, LeGO-LOAM [2], and our algorithm achieves real time.

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ICRA Conference 2020 Conference Paper

A Fast and Accurate Solution for Pose Estimation from 3D Correspondences

Lipu Zhou
Shengze Wang 0002
Michael Kaess

Estimating pose from given 3D correspondences, including point-to-point, point-to-line and point-to-plane correspondences, is a fundamental task in computer vision with many applications. We present a fast and accurate solution for the least-squares problem of this task. Previous works mainly focus on studying the way to find the global minimizer of the least-squares problem. However, existing works that show the ability to achieve the global minimizer are still unsuitable for real-time applications. Furthermore, as one of contributions of this paper, we prove that there exist ambiguous configurations for any number of lines and planes. These configurations have several solutions in theory, which makes the correct solution may come from a local minimizer when the data are with noise. Previous works based on convex optimization which is unable to find local minimizers do not work in the ambiguous configuration. Our algorithm is efficient and able to reveal local minimizers. We employ the Cayley-Gibbs-Rodriguez (CGR) parameterization of the rotation to derive a general rational cost for the three cases of 3D correspondences. The main contribution of this paper is to solve the first-order optimality conditions of the least-squares problem, which are of a complicated rational form. The central idea of our algorithm is to introduce some intermediate unknowns to simplify the problem. Extensive experimental results show that our algorithm is more stable than previous algorithms when the number N of correspondences is small. Besides, when N is large, our algorithm achieves the same accuracy as the state-of-the-art algorithm [1], but our algorithm is about 7 times faster than [1] in real applications.

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