Appearance-based loop detection from 3D laser data using the normal distributions transform

Magnusson, Martin; Andreasson, Henrik; Nüchter, Andreas; Lilienthal, Achim J.

doi:10.1109/robot.2009.5152712

Cited by 121 publications

(120 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

333

253

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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

333

253

View full text Add to dashboard Cite

show abstract

“…While our computational requirements are higher than those of other state-of-the-art systems, our recall rate on the considered datasets is substantially higher. In future work, we plan to substantially reduce the computational cost of our method by introducing fast pre-selection methods for the scan candidates, e.g., using a bag-of-words approach like Cummins and Newman [5] or by using the approach of Magnusson et al [14] with a high difference threshold as a preprocessing method.…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, we used the freely available Hanover2 dataset (Courtesy of Oliver Wulf) which has also been used by Magnusson et al [14] to evaluate their place recognition system. It contains 923 3D scans acquired along a 1.24 km long path.…”

Section: Methodsmentioning

confidence: 99%

“…Recently, Magnusson et al [14] proposed a system for place recognition based on 3D data. They utilize the Normal Distribution Transform as features to match scans.…”

Section: Related Workmentioning

confidence: 99%

“…Magnusson et al [14] used a distance of 10 m as the visibility constraint for the evaluation of their recall rate of 35.3%. For this distance our recall rate is 58.1%.…”

Section: B Hanover2 Datasetmentioning

confidence: 99%

See 2 more Smart Citations

Robust place recognition for 3D range data based on point features

Steder

Grisetti

Burgard

2010

2010 IEEE International Conference on Robotics and Automation

114

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Abstract-The problem of place recognition appears in different mobile robot navigation problems including localization, SLAM, or change detection in dynamic environments. Whereas this problem has been studied intensively in the context of robot vision, relatively few approaches are available for threedimensional range data. In this paper, we present a novel and robust method for place recognition based on range images. Our algorithm matches a given 3D scan against a database using point features and scores potential transformations by comparing significant points in the scans. A further advantage of our approach is that the features allow for a computation of the relative transformations between scans which is relevant for registration processes. Our approach has been implemented and tested on different 3D data sets obtained outdoors. In several experiments we demonstrate the advantages of our approach also in comparison to existing techniques.

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Automatic appearance‐based loop detection from three‐dimensional laser data using the normal distributions transform

Magnusson

Andreasson

Nüchter

et al. 2009

Journal of Field Robotics

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We propose a new approach to appearance-based loop detection for mobile robots, using three-dimensional (3D) laser scans. Loop detection is an important problem in the simultaneous localization and mapping (SLAM) domain, and, because it can be seen as the problem of recognizing previously visited places, it is an example of the data association problem. Without a flat-floor assumption, two-dimensional laser-based approaches are bound to fail in many cases. Two of the problems with 3D approaches that we address in this paper are how to handle the greatly increased amount of data and how to efficiently obtain invariance to 3D rotations. We present a compact representation of 3D point clouds that is still discriminative enough to detect loop closures without false positives (i.e., detecting loop closure where there is none). A low false-positive rate is very important because wrong data association could have disastrous consequences in a SLAM algorithm. Our approach uses only the appearance of 3D point clouds to detect loops and requires no pose information. We exploit the normal distributions transform surface representation to create feature histograms based on surface orientation and smoothness. The surface shape histograms compress the input data by two to three orders of magnitude. Because of the high compression rate, the histograms can be matched efficiently to compare the appearance of two scans. Rotation invariance is achieved by aligning scans with respect to dominant surface orientations. We also propose to use expectation maximization to fit Magnusson et al.: Automatic Appearance-Based Loop Detection from 3D Laser Data Using NDT • 893a gamma mixture model to the output similarity measures in order to automatically determine the threshold that separates scans at loop closures from nonoverlapping ones. We discuss the problem of determining ground truth in the context of loop detection and the difficulties in comparing the results of the few available methods based on range information. Furthermore, we present quantitative performance evaluations using three realworld data sets, one of which is highly self-similar, showing that the proposed method achieves high recall rates (percentage of correctly identified loop closures) at low falsepositive rates in environments with different characteristics. C 2009 Wiley Periodicals, Inc.

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Appearance-based loop detection from 3D laser data using the normal distributions transform

Cited by 121 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

Robust place recognition for 3D range data based on point features

Automatic appearance‐based loop detection from three‐dimensional laser data using the normal distributions transform

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