Robust LiDAR-based localization in architectural floor plans

Boniardi, Federico; Caselitz, Tim; Kümmerle, Rainer; Burgard, Wolfram

doi:10.1109/iros.2017.8206168

Cited by 50 publications

(26 citation statements)

References 17 publications

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“…Biswas and Veloso [22] have used floor-plan matching with line segments representing walls to improve robot navigation. Boniardi et al [23] also make use of floor plans by applying SLAM to LiDAR scans but require an initial state for incremental position correction, making the localization local instead of global. Fast global indoor localization without external sensors is still an open problem.…”

Section: Localization Without External Sensorsmentioning

confidence: 99%

Indoor Localization for Augmented Reality Devices Using BIM, Point Clouds, and Template Matching

Herbers

König

2019

Applied Sciences

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Mobile devices are a common target for augmented reality applications, especially for showing contextual information in buildings or construction sites. A prerequisite of contextual information display is the localization of objects and the device in the real world. In this paper, we present our approach to the problem of mobile indoor localization with a given building model. The approach does not use external sensors or input. Accurate external sensors such as stationary cameras may be expensive and difficult to set up and maintain. Relying on already existing external sources may also prove to be difficult, as especially inside buildings, Internet connections can be unreliable and GPS signals can be inaccurate. Therefore, we try to find a localization solution for augmented reality devices that can accurately localize itself only with data from internal sensors and preexisting information about the building. If a building has an accurate model of its geometry, we can use modern spatial mapping techniques and point-cloud matching to find a mapping between local device coordinates and global model coordinates. We use normal analysis and 2D template matching on an inverse distance map to determine this mapping. The proposed algorithm is designed to have a high speed and efficiency, as mobile devices are constrained by hardware limitations. We show an implementation of the algorithm on the Microsoft HoloLens, test the localization accuracy, and offer use cases for the technology.

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Section: Localization Without External Sensorsmentioning

confidence: 99%

Indoor Localization for Augmented Reality Devices Using BIM, Point Clouds, and Template Matching

Herbers

König

2019

Applied Sciences

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“…Boniardi et al [17] presented a localization that uses CAD floor plans as prior for SLAM. They used localization alone if enough associations between the scans and the floor plan can be obtained in the vicinity of the robot, otherwise, they used full pose-graph optimization.…”

Section: Localization In Prior Mapsmentioning

confidence: 99%

“…Most previous works use prior information to enhance SLAM by assuming metrically accurate priors [6,9,10,12,14], e.g., aerial images or CAD drawings, or are able to handle some inaccuracies in the prior map [7,17]. However, they do not correct both the prior and the sensor map, even though both map types can suffer from inaccuracies.…”

Section: Graph Based Slam With a Layout Map As Priormentioning

confidence: 99%

The Auto-Complete Graph: Merging and Mutual Correction of Sensor and Prior Maps for SLAM

2019

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Simultaneous Localization And Mapping (SLAM) usually assumes the robot starts without knowledge of the environment. While prior information, such as emergency maps or layout maps, is often available, integration is not trivial since such maps are often out of date and have uncertainty in local scale. Integration of prior map information is further complicated by sensor noise, drift in the measurements, and incorrect scan registrations in the sensor map. We present the Auto-Complete Graph (ACG), a graph-based SLAM method merging elements of sensor and prior maps into one consistent representation. After optimizing the ACG, the sensor map’s errors are corrected thanks to the prior map, while the sensor map corrects the local scale inaccuracies in the prior map. We provide three datasets with associated prior maps: two recorded in campus environments, and one from a fireman training facility. Our method handled up to 40% of noise in odometry, was robust to varying levels of details between the prior and the sensor map, and could correct local scale errors of the prior. In field tests with ACG, users indicated points of interest directly on the prior before exploration. We did not record failures in reaching them.

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“…Few methods have been developed to match maps with a high level of abstraction. Instead, most works focus on matching partial metric maps or limit the extent to which maps can differ by using particular types of maps such as CAD models, aerial images, or blueprint layouts [4][5][6][7][8]. It is easy to understand why that is: similar maps can use easily describable similarity measures based on direct sensor measurements or some classic descriptors such as SIFT.…”

Section: Related Workmentioning

confidence: 99%

“…Some works use CAD maps representing a building in a metrically correct way. For example, Boniardi et al [8] presented a method for localization using a CAD floor plan as prior map. They performed matching between maps from different modalities by using maximum a posteriori pose-graph-based SLAM system [15].…”

Section: Related Workmentioning

confidence: 99%

URSIM: Unique Regions for Sketch Map Interpretation and Matching

2019

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We present a method for matching sketch maps to a corresponding metric map, with the aim of later using the sketch as an intuitive interface for human-robot interactions. While sketch maps are not metrically accurate and many details, which are deemed unnecessary, are omitted, they represent the topology of the environment well and are typically accurate at key locations. Thus, for sketch map interpretation and matching, one cannot only rely on metric information. Our matching method first finds the most distinguishable, or unique, regions of two maps. The topology of the maps, the positions of the unique regions, and the size of all regions are used to build region descriptors. Finally, a sequential graph matching algorithm uses the region descriptors to find correspondences between regions of the sketch and metric maps. Our method obtained higher accuracy than both a state-of-the-art matching method for inaccurate map matching, and our previous work on the subject. The state of the art was unable to match sketch maps while our method performed only 10% worse than a human expert.

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Robust LiDAR-based localization in architectural floor plans

Cited by 50 publications

References 17 publications

Indoor Localization for Augmented Reality Devices Using BIM, Point Clouds, and Template Matching

Indoor Localization for Augmented Reality Devices Using BIM, Point Clouds, and Template Matching

The Auto-Complete Graph: Merging and Mutual Correction of Sensor and Prior Maps for SLAM

URSIM: Unique Regions for Sketch Map Interpretation and Matching

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