Dense planar SLAM

Salas-Moreno, Renato F.; Glocken, Ben; Kelly, Paul H. J.; Davison, Andrew J.

doi:10.1109/ismar.2014.6948422

Cited by 176 publications

(112 citation statements)

References 23 publications

(37 reference statements)

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“…The surfel-based representation allows to model comparably large environments without a compromise in terms of reduced reconstruction fidelity due to reduced grid resolution or a more complex implementation [32,19]. Other recent RGB-D approaches use the surfels to extract planar surfaces [24] or perform online loop closure integration by deforming the surfel representation [33,31].…”

Section: Related Workmentioning

confidence: 99%

Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments

Behley¹,

Stachniss²

2018

Robotics: Science and Systems XIV

389

264

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Abstract-Accurate and reliable localization and mapping is a fundamental building block for most autonomous robots. For this purpose, we propose a novel, dense approach to laserbased mapping that operates on three-dimensional point clouds obtained from rotating laser sensors. We construct a surfel-based map and estimate the changes in the robot's pose by exploiting the projective data association between the current scan and a rendered model view from that surfel map. For detection and verification of a loop closure, we leverage the map representation to compose a virtual view of the map before a potential loop closure, which enables a more robust detection even with low overlap between the scan and the already mapped areas. Our approach is efficient and enables real-time capable registration. At the same time, it is able to detect loop closures and to perform map updates in an online fashion. Our experiments show that we are able to estimate globally consistent maps in large scale environments solely based on point cloud data.I. INTRODUCTION Most autonomous robots, including self-driving cars, must be able to reliably localize themselves, ideally by using only their own sensors without relying on external information such as GPS or other additional infrastructure placed in the environment. There has been significant advances in visionbased [6,7] and RGB-D-based [18,33,3] SLAM systems over the past few years. Most of these approaches use (semi-)dense reconstructions of the environment and exploit them for frameto-model tracking, either by jointly optimizing the map and pose estimates or by alternating pose estimation and map building [21]. Dense approaches have a prospective advantage over feature-based and sparse approaches as they use all available information and thus do not depend on reliable feature extraction or availability of such features.In contrast to these developments, current 3D laser-based mapping systems mainly accomplish the estimation relying on feature-based solutions [34,35], reduced map representations [14,13], voxel grid-based methods [16], or point sub-sampling [30], which all effectively reduce the data used for alignment. Compared to most indoor applications using RGB-D sensors, we have to tackle additional challenges in outdoor applications using 3D laser sensors, i.e., (1) fast sensor movement resulting in large displacements between scans, (2) comparably sparse point clouds, and (3) large-scale environments.In this paper, we present a dense mapping approach called Surfel-based Mapping (SuMa), which builds globally consistent maps by tracking the pose, so-called odometry, and closing loops using a pose graph as illustrated in Figure 1. To this end, we employ a surfel map to efficiently generate synthetic views for projective data association and additional

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Section: Related Workmentioning

confidence: 99%

Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments

Behley¹,

Stachniss²

2018

Robotics: Science and Systems XIV

389

264

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“…Furthermore, there exist alternative processing models, including directly leveraging RGB-D data and foregoing the need for a mesh model. Surfaces for displaying content might be detected during geometry recovery using techniques such as Dense Planar SLAM [181] or Parallel Tracking and Mapping [113].…”

Section: Summary and Future Workmentioning

confidence: 99%

The personal cockpit

Ens

Finnegan

Irani

2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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“…Loop closure is detected when previously seen region is revisited and a pose graph is built and optimized to create a globally consistent map in [13], as well as in the work of Endres et al [7,16], Whelan et al [17,18] and Kerl et al [19]. Moreover, higher-level primitives such as edges [20,21], occluding contours [22], lines [23] and planes [24][25][26][27] are used as additional information to constrain the pose estimation process.…”

Section: Related Workmentioning

confidence: 99%

CuFusion: Accurate Real‐time Camera Tracking and Volumetric Scene Reconstruction with a Cuboid

Zhang

2017

Preprint

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Given a stream of depth images with a known cuboid reference object present in the scene, we propose a novel approach for accurate camera tracking and volumetric surface reconstruction in real-time. Our contribution in this paper is threefold: (a) utilizing a priori knowledge of the cuboid reference object, we keep drift-free camera tracking without explicit global optimization; (b) we improve the fineness of the volumetric surface representation by proposing a prediction-corrected data fusion strategy rather than simple moving average, which enables accurate reconstruction of high-frequency details such as sharp edges of objects and geometries of high curvature; (c) we introduce a benchmark dataset CU3D containing both synthetic and real-world scanning sequences with ground-truth camera trajectories and surface models for quantitative evaluation of 3D reconstruction algorithms. We test our algorithm on our dataset and demonstrate its accuracy compared with other state-of-the-art algorithms. We release both our dataset and code as opensource 1 for other researchers to reproduce and verify our results.

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Dense planar SLAM

Cited by 176 publications

References 23 publications

Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments

Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments

The personal cockpit

CuFusion: Accurate Real‐time Camera Tracking and Volumetric Scene Reconstruction with a Cuboid

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