CPA-SLAM: Consistent plane-model alignment for direct RGB-D SLAM

Ma, Lingni; Kerl, Christian; Stückler, Jörg; Cremers, Daniel

doi:10.1109/icra.2016.7487260

Cited by 137 publications

(89 citation statements)

References 19 publications

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“…In [91], a dense SLAM method is proposed based on dense ICP with a piecewise planar map. In both [73] and [45], it is proposed to model planes in a global map, so that they are optimized together with the keyframe poses in the graph optimization for global consistency. The main difference is that the former uses direct image alignment in an EM framework, while the latter combines geometric and photometric methods for the fast odometry estimation.…”

Section: Plane-based Methodsmentioning

confidence: 99%

RGB-D Odometry and SLAM

Civera

Lee

2019

RGB-D Image Analysis and Processing

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The emergence of modern RGB-D sensors had a significant impact in many application fields, including robotics, augmented reality (AR) and 3D scanning. They are low-cost, low-power and low-size alternatives to traditional range sensors such as LiDAR. Moreover, unlike RGB cameras, RGB-D sensors provide the additional depth information that removes the need of frame-by-frame triangulation for 3D scene reconstruction. These merits have made them very popular in mobile robotics and AR, where it is of great interest to estimate egomotion and 3D scene structure. Such spatial understanding can enable robots to navigate autonomously without collisions and allow users to insert virtual entities consistent with the image stream. In this chapter, we review common formulations of odometry and Simultaneous Localization and Mapping (known by its acronym SLAM) using RGB-D stream input. The two topics are closely related, as the former aims to track the incremental camera motion with respect to a local map of the scene, and the latter to jointly estimate the camera trajectory and the global map with consistency. In both cases, the standard approaches minimize a cost function using nonlinear optimization techniques. This chapter consists of three main parts: In the first part, we introduce the basic concept of odometry and SLAM and motivate the use of RGB-D sensors. We also give mathematical preliminaries relevant to most odometry and SLAM algorithms. In the second part, we detail the three main components of SLAM systems: camera pose tracking, scene mapping and loop closing. For each component, we describe different approaches proposed in the literature. In the final part, we provide a brief discussion on advanced research topics with the references to the state-of-the-art.

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Section: Plane-based Methodsmentioning

confidence: 99%

RGB-D Odometry and SLAM

Civera

Lee

2019

RGB-D Image Analysis and Processing

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“…Multimodal correspondences play also an important role in the extrinsic calibration of sensors of different nature, such as camera and lidar [51,49,16,18]. Numerous state-of-the-art SLAM (Simultaneous Localization and Mapping) solutions work with models that include plane and line primitives too [2,47,17,43,28,19].…”

Section: Multimodal Registrationmentioning

confidence: 99%

“…It seeks the transformation that brings closest together different surfaces according to some meaningful distance function. Consider the ubiquituous scenario in which a system (usually a sensor) returns 3D points {x i } m i=1 of an object and a model of the same is available consisting of 3D primitives {P i } (typically points, lines and/or planes) [3,14,47,28]. Assuming the correspondences between the Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Convex Global 3D Registration with Lagrangian Duality

Briales

González-Jiménez

2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

136

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The registration of 3D models by a Euclidean transformation is a fundamental task at the core of many application in computer vision. This problem is non-convex due to the presence of rotational constraints, making traditional local optimization methods prone to getting stuck in local minima. This paper addresses finding the globally optimal transformation in various 3D registration problems by a unified formulation that integrates common geometric registration modalities (namely point-to-point, point-to-line and point-to-plane). This formulation renders the optimization problem independent of both the number and nature of the correspondences.The main novelty of our proposal is the introduction of a strengthened Lagrangian dual relaxation for this problem, which surpasses previous similar approaches [32] in effectiveness. In fact, even though with no theoretical guarantees, exhaustive empirical evaluation in both synthetic and real experiments always resulted on a tight relaxation that allowed to recover a guaranteed globally optimal solution by exploiting duality theory.Thus, our approach allows for effectively solving the 3D registration with global optimality guarantees while running at a fraction of the time for the state-of-the-art alternative [34], based on a more computationally intensive Branch and Bound method.

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“…Elqursh and Elgammal [5] introduce a linebased camera pose estimation method, while Koch et al [14] use 3D line segments to align the non-overlapping indoor and outdoor reconstructions. Planar patch detection and matching [34,20,4,28,31,7,17] are significantly used strategies to improve the reconstruction accuracy. Some works [34,20,4,28] exploit plane correspondence to solve for frame-to-frame camera poses.…”

Section: Related Workmentioning

confidence: 99%

Floorplan-Jigsaw: Jointly Estimating Scene Layout and Aligning Partial Scans

Cheng

Wang

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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We present a novel approach to align partial 3D reconstructions which may not have substantial overlap. Using floorplan priors, our method jointly predicts a room layout and estimates the transformations from a set of partial 3D data. Unlike the existing methods relying on feature descriptors to establish correspondences, we exploit the 3D "box" structure of a typical room layout that meets the Manhattan World property. We first estimate a local layout for each partial scan separately and then combine these local layouts to form a globally aligned layout with loop closure. Without the requirement of feature matching, the proposed method enables some novel applications ranging from large or featureless scene reconstruction and modeling from sparse input. We validate our method quantitatively and qualitatively on real and synthetic scenes of various sizes and complexities. The evaluations and comparisons show superior effectiveness and accuracy of our method.

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CPA-SLAM: Consistent plane-model alignment for direct RGB-D SLAM

Cited by 137 publications

References 19 publications

RGB-D Odometry and SLAM

RGB-D Odometry and SLAM

Convex Global 3D Registration with Lagrangian Duality

Floorplan-Jigsaw: Jointly Estimating Scene Layout and Aligning Partial Scans

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