Real-time loop closure in 2D LIDAR SLAM

Heß, W. R.; Kohler, Damon; Rapp, Holger; Andor, Daniel

doi:10.1109/icra.2016.7487258

Cited by 1,599 publications

(953 citation statements)

References 19 publications

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“…Essentially, SLAM allows the 3D reconstruction of the interior physical map while an individual or robot is moving freely and being continuously tracked inside an unknown indoor environment. SLAM technology spans from expensive systems based on Light Imaging, Detection, And Ranging (LIDAR) technology including [249]- [252] that deliver centimeter-level mapping accuracy to cost-effective smartphone-based solutions that achieve meter-level accuracy. Between these two extremes, camera-based solutions, especially those developed around Microsoft Kinect including [253]- [256], provide a good balance between cost (in terms of equipment price and survey time) and map accuracy.…”

Section: A Simultaneous Localization and Mappingmentioning

confidence: 99%

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Laoudias¹,

Moreira

Kim

et al. 2018

IEEE Commun. Surv. Tutorials

369

171

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Abstract-Location information for events, assets, and individuals, mostly focusing on two dimensions so far, has triggered a multitude of applications across different verticals, such as consumer, networking, industrial, health care, public safety, and emergency response use cases. To fully exploit the potential of location awareness and enable new advanced location-based services, localization algorithms need to be combined with complementary technologies including accurate height estimation, i.e., three dimensional location, reliable user mobility classification, and efficient indoor mapping solutions. This survey provides a comprehensive review of such enabling technologies. In particular, we present cellular localization systems including recent results on 5G localization, and solutions based on Wireless Local Area Networks (WLAN), highlighting those that are capable of computing 3D location in multi-floor indoor environments. We overview range-free localization schemes, which have been traditionally explored in Wireless Sensor Networks (WSN) and are nowadays gaining attention for several envisioned Internet of Things (IoT) applications. We also present user mobility estimation techniques, particularly those applicable in cellular networks, that can improve localization and tracking accuracy. Regarding the mapping of physical space inside buildings for aiding tracking and navigation applications, we study recent advances and focus on smartphone-based indoor Simultaneous Localization and Mapping (SLAM) approaches.The survey concludes with service availability and system scalability considerations, as well as security and privacy concerns in location architectures, discusses the technology roadmap, and identifies future research directions.

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Section: A Simultaneous Localization and Mappingmentioning

confidence: 99%

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Laoudias¹,

Moreira

Kim

et al. 2018

IEEE Commun. Surv. Tutorials

369

171

View full text Add to dashboard Cite

show abstract

“…The utilized algorithms for solving SLAM in 2D have been described in a recent paper by the authors of the software (Hess et al, 2016). It can deliver impressive results -especially considering that it runs in real-time on commodity hardware.…”

Section: Introductionmentioning

confidence: 99%

“…This operation is done in O(n) with n being the number of obstructed grid points in each precomputed grid. Hess et al (2016) describe the 2D version of the algorithm, which uses the horizontally mounted 2D profiler. The provided data sets contain also data from a setup with Velodyne PUCK scanners (cf.…”

Section: Calibration Referencing and Slammentioning

confidence: 99%

Improving Google's Cartographer 3d Mapping by Continuous-Time Slam

Nüchter

Bleier

Schauer

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper shows how to use the result of Google's SLAM solution, called Cartographer, to bootstrap our continuous-time SLAM algorithm. The presented approach optimizes the consistency of the global point cloud, and thus improves on Google's results. We use the algorithms and data from Google as input for our continuous-time SLAM software. We also successfully applied our software to a similar backpack system which delivers consistent 3D point clouds even in absence of an IMU.

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“…These behaviors are similar to the prediction and update process in SLAM. Different from the existing SLAM algorithms based on probabilistic methods [17,18] , RatSLAM [19] , a brain-inspired SLAM algorithm, creates a cognitive map based on visual information and the information of its previous experiences. Rat-SLAM simulates the rodent s spatial cognition mechanism using pose cells, local view cells and cognitive maps.…”

Section: Research Article Special Issue On Human-inspired Computingmentioning

confidence: 99%

A brain-inspired SLAM system based on ORB features

Zhou

Yan

et al. 2017

Int. J. Autom. Comput.

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This paper describes a brain-inspired simultaneous localization and mapping (SLAM) system using oriented features from accelerated segment test and rotated binary robust independent elementary (ORB) features of RGB (red, green, blue) sensor for a mobile robot. The core SLAM system, dubbed RatSLAM, can construct a cognitive map using information of raw odometry and visual scenes in the path traveled. Different from existing RatSLAM system which only uses a simple vector to represent features of visual image, in this paper, we employ an efficient and very fast descriptor method, called ORB, to extract features from RGB images. Experiments show that these features are suitable to recognize the sequences of familiar visual scenes. Thus, while loop closure errors are detected, the descriptive features will help to modify the pose estimation by driving loop closure and localization in a map correction algorithm. Efficiency and robustness of our method are also demonstrated by comparing with different visual processing algorithms.Keywords: Simultaneous localization and mapping (SLAM), RatSLAM, mobile robot, oriented features from accelerated segment test and rotated binary robust independent elementary (ORB) features of RGB (red, green, blue), cognitive map.

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Real-time loop closure in 2D LIDAR SLAM

Cited by 1,599 publications

References 19 publications

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Improving Google's Cartographer 3d Mapping by Continuous-Time Slam

A brain-inspired SLAM system based on ORB features

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