Keyframe-based dense planar SLAM

Hsiao, Ming; Westman, Eric; Kaess, Michael

doi:10.1109/icra.2017.7989597

Cited by 118 publications

(67 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

Section: Plane-based Methodsmentioning

confidence: 99%

RGB-D Odometry and SLAM

Civera

Lee

2019

RGB-D Image Analysis and Processing

View full text Add to dashboard Cite

show abstract

“…Lee [12] estimates the layout plane and point cloud iteratively to reduce mapping drift. Similarly, planes are shown to provide long-range SLAM constraints compared to points in indoor building environments [25] [26]. Recently, [27] proposes similar work to jointly optimize objects, planes, points with camera poses.…”

Section: B Object and Plane Slammentioning

confidence: 99%

Monocular Object and Plane SLAM in Structured Environments

Yang

Scherer

2019

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

In this paper, we present a monocular Simultaneous Localization and Mapping (SLAM) algorithm using high-level object and plane landmarks. The built map is denser, more compact and semantic meaningful compared to feature point based SLAM. We first propose a high order graphical model to jointly infer the 3D object and layout planes from single images considering occlusions and semantic constraints. The extracted objects and planes are further optimized with camera poses in a unified SLAM framework. Objects and planes can provide more semantic constraints such as Manhattan plane and object supporting relationships compared to points. Experiments on various public and collected datasets including ICL NUIM and TUM Mono show that our algorithm can improve camera localization accuracy compared to state-of-the-art SLAM especially when there is no loop closure, and also generate dense maps robustly in many structured environments.

show abstract

“…One of the earliest methods by Dou et al [7] combines both plane and feature point correspondences to estimate camera poses in RGB-D scanning. A recent work by Hsiao et al [12] uses very similar idea to introduce plane constraint into tracking in SLAM framework, and their result exceeds current stateof-the-art online 3D reconstruction methods in pose estimation. For offline reconstruction framework, a very recent work by Halber and Funkhouser [10] proposes a fineto-coarse global registration algorithm on RGB-D data by combining planar relationship constraints with other types of constraints, and their method works efficiently on largescale RGB-D data.…”

Section: Related Workmentioning

confidence: 99%

Plane-Based Optimization of Geometry and Texture for RGB-D Reconstruction of Indoor Scenes

Wang

Guo

2018

2018 International Conference on 3D Vision (3DV)

View full text Add to dashboard Cite

We present a novel approach to reconstruct RGB-D indoor scene with plane primitives. Our approach takes as input a RGB-D sequence and a dense coarse mesh reconstructed by some 3D reconstruction method on the sequence, and generate a lightweight, low-polygonal mesh with clear face textures and sharp features without losing geometry details from the original scene. To achieve this, we firstly partition the input mesh with plane primitives, simplify it into a lightweight mesh next, then optimize plane parameters, camera poses and texture colors to maximize the photometric consistency across frames, and finally optimize mesh geometry to maximize consistency between geometry and planes. Compared to existing planar reconstruction methods which only cover large planar regions in the scene, our method builds the entire scene by adaptive planes without losing geometry details and preserves sharp features in the final mesh. We demonstrate the effectiveness of our approach by applying it onto several RGB-D scans and comparing it to other state-of-the-art reconstruction methods.

show abstract

Keyframe-based dense planar SLAM

Cited by 118 publications

References 25 publications

RGB-D Odometry and SLAM

RGB-D Odometry and SLAM

Monocular Object and Plane SLAM in Structured Environments

Plane-Based Optimization of Geometry and Texture for RGB-D Reconstruction of Indoor Scenes

Contact Info

Product

Resources

About