A fast histogram-based similarity measure for detecting loop closures in 3-D LIDAR data

Röhling, Timo; Mack, Jennifer; Schulz, Dirk

doi:10.1109/iros.2015.7353454

Cited by 91 publications

(41 citation statements)

References 11 publications

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“…For loop closure detection using three-dimensional laser-range data, mainly feature-based approaches [25,26,22] were investigated. These approaches either use specific interest points [25,26] to aggregate features or generally generate a global feature representation [15,22] of a point cloud, which is then used to compute a distance between two point clouds. Often, a simple thresholding is used to identify potential loop closure candidates, which then must be verified.…”

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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“…Using global descriptors of the local point cloud for place recognition was also proposed in Röhling et al (2015), Granström et al (2011), Magnusson et al (2009), and Cop et al (2018). Röhling et al (2015) proposed describing each local point cloud with a 1D histogram of point heights, assuming that the sensor keeps a constant height above the ground. The histograms were then compared using the Wasserstein metric for recognizing places.…”

Section: Localization In 3d Point Cloudsmentioning

confidence: 99%

SegMap: Segment-based mapping and localization using data-driven descriptors

Dubé

Cramariuc

Dugas

et al. 2019

The International Journal of Robotics Research

173

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Precisely estimating a robot’s pose in a prior, global map is a fundamental capability for mobile robotics, e.g., autonomous driving or exploration in disaster zones. This task, however, remains challenging in unstructured, dynamic environments, where local features are not discriminative enough and global scene descriptors only provide coarse information. We therefore present SegMap: a map representation solution for localization and mapping based on the extraction of segments in 3D point clouds. Working at the level of segments offers increased invariance to view-point and local structural changes, and facilitates real-time processing of large-scale 3D data. SegMap exploits a single compact data-driven descriptor for performing multiple tasks: global localization, 3D dense map reconstruction, and semantic information extraction. The performance of SegMap is evaluated in multiple urban driving and search and rescue experiments. We show that the learned SegMap descriptor has superior segment retrieval capabilities, compared with state-of-the-art handcrafted descriptors. As a consequence, we achieve a higher localization accuracy and a 6% increase in recall over state-of-the-art handcrafted descriptors. These segment-based localizations allow us to reduce the open-loop odometry drift by up to 50%. SegMap is open-source available along with easy to run demonstrations.

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“…Histogram extracts the feature values of points and encodes them as descriptors using global features [ 58 , 59 , 60 ] or selected keypoints [ 61 , 62 , 63 , 64 ]. One of the approaches used by these methods is the normal distribution transform (NDT) histogram [ 65 ], [ 66 ] which provides the compact representation of point cloud maps into a set of normal distributions.…”

Section: Taxonomy Of Loop Closure Detectionmentioning

confidence: 99%

“…To overcome the computational overhead, many researchers have put efforts into developing fast loop detection methods. In [ 58 ], the performance of the loop detection method presented in [ 67 ] has been improved and the computational cost is reduced by using the similarity measure histograms extracted from Lidar scans that are independent of NDT. Lin et al [ 70 ] developed a fast loop closure detection system for Lidar odometry and mapping.…”

Section: Taxonomy Of Loop Closure Detectionmentioning

confidence: 99%

Role of Deep Learning in Loop Closure Detection for Visual and Lidar SLAM: A Survey

Arshad

Kim

2021

Sensors

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Loop closure detection is of vital importance in the process of simultaneous localization and mapping (SLAM), as it helps to reduce the cumulative error of the robot’s estimated pose and generate a consistent global map. Many variations of this problem have been considered in the past and the existing methods differ in the acquisition approach of query and reference views, the choice of scene representation, and associated matching strategy. Contributions of this survey are many-fold. It provides a thorough study of existing literature on loop closure detection algorithms for visual and Lidar SLAM and discusses their insight along with their limitations. It presents a taxonomy of state-of-the-art deep learning-based loop detection algorithms with detailed comparison metrics. Also, the major challenges of conventional approaches are identified. Based on those challenges, deep learning-based methods were reviewed where the identified challenges are tackled focusing on the methods providing long-term autonomy in various conditions such as changing weather, light, seasons, viewpoint, and occlusion due to the presence of mobile objects. Furthermore, open challenges and future directions were also discussed.

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A fast histogram-based similarity measure for detecting loop closures in 3-D LIDAR data

Cited by 91 publications

References 11 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

SegMap: Segment-based mapping and localization using data-driven descriptors

Role of Deep Learning in Loop Closure Detection for Visual and Lidar SLAM: A Survey

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