Large-scale direct SLAM with stereo cameras

Engel, Jakob; Stückler, Jörg; Cremers, Daniel

doi:10.1109/iros.2015.7353631

Cited by 445 publications

(278 citation statements)

References 15 publications

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“…We included here for comparison the reported results of LOAM [35], a laser-based odometry approach, and for indirect comparison with stateof-the-art vision approaches, Stereo LSD-SLAM [6], a stereo vision-based complete SLAM system with pose graph optimization, and the currently best performing approach SOFT SLAM [2], also a stereo vision-based approach.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…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].…”

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confidence: 99%

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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: Experimental Evaluationmentioning

confidence: 99%

mentioning

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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“…The main drawback of this category of methods is that drift is accumulated over time. As a result, after the mobile platform has travelled a certain distance, the localization error becomes significant, making the localization result unusable over longer distances (Engel et al, 2015). In parallel to the VO approaches, there are a number of methods that perform visual-SLAM (Lategahn et al, 2011).…”

Section: Related Workmentioning

confidence: 99%

GPS-Denied Geo-Localisation Using Visual Odometry

Gupta

Chang

Yilmaz

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The primary method for geo-localization is based on GPS which has issues of localization accuracy, power consumption, and unavailability. This paper proposes a novel approach to geo-localization in a GPS-denied environment for a mobile platform. Our approach has two principal components: public domain transport network data available in GIS databases or OpenStreetMap; and a trajectory of a mobile platform. This trajectory is estimated using visual odometry and 3D view geometry. The transport map information is abstracted as a graph data structure, where various types of roads are modelled as graph edges and typically intersections are modelled as graph nodes. A search for the trajectory in real time in the graph yields the geo-location of the mobile platform. Our approach uses a simple visual sensor and it has a low memory and computational footprint. In this paper, we demonstrate our method for trajectory estimation and provide examples of geolocalization using public-domain map data. With the rapid proliferation of visual sensors as part of automated driving technology and continuous growth in public domain map data, our approach has the potential to completely augment, or even supplant, GPS based navigation since it functions in all environments.

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“…In the latest decades, there has been an intense research on vision navigation, which can be divided into two main techniques: feature-based methods [1][2][3][4][5] and direct methods [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Vision Navigation Based on the Fusion of Feature-based Methods and Direct Methods

Cheng¹,

Hao²,

Li³

et al. 2018

dtcse

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Abstract. Vision navigation is an alternative to Global Position System (GPS) in environments where access to GPS is denied. Yet, the classical feature-based method has to extract feature observations from the image as input, which performs poor in environments with little features. To address this problem, a fusion of feature-based method and direct method is designed. The feature-based method is used in feature rich regions, while the direct method is used in regions with little features. Thus we utilize the fusion of two methods to improve the environmental adaptability of vision navigation. To improve the robustness to outliers, the Huber weight function is applied. Then the nonlinear optimization method is utilized to obtain the optimal camera pose. Experimental results demonstrate that the proposed method can meet the needs of real-time autonomous navigation.

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Large-scale direct SLAM with stereo cameras

Cited by 445 publications

References 15 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

GPS-Denied Geo-Localisation Using Visual Odometry

Vision Navigation Based on the Fusion of Feature-based Methods and Direct Methods

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