Connecting Semantic Building Information Models and Robotics: An application to 2D LiDAR-based localization

Hendrikx, R. W. M.; Pauwels, Pieter; Torta, Elena; Bruyninckx, Herman; Molengraft, M.J.G. van de

doi:10.1109/icra48506.2021.9561129

Cited by 19 publications

(13 citation statements)

References 18 publications

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“…These deep learning-based methods rely on a pre-trained neural network for feature extraction and pose regression, in which the pose estimation module lacked interpretability, bringing difficulties for debugging and deployment in the real world. Researchers in [29] extracted semantic features without learning and also performed robot localization in BIM using 2D laser scans. The results showed that the robot can track its pose in BIM but the localization performance was not evaluated quantitatively.…”

Section: Bim-based Pose Estimationmentioning

confidence: 99%

Semantic localization on BIM-generated maps using a 3D LiDAR sensor

Yin¹,

Lin²,

Yeoh³

2022

Preprint

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Conventional sensor-based localization relies on high-precision maps. These maps are generally built using specialized mapping techniques, which involve high labor and computational costs. While in the architectural, engineering and construction industry, building information models (BIMs) are available and can provide informative descriptions of environments. This paper explores an effective way to localize a mobile 3D LiDAR sensor in BIM considering both geometric and semantic properties. Specifically, we first convert original BIM to semantic maps using categories and locations of BIM elements. After that, a coarse-to-fine semantic localization is performed to align laser points to the map via iterative closest point registration. The experimental results show that the semantic localization can track the pose with only scan matching and present centimeter-level errors over 340 meters traveling, thus demonstrating the feasibility of the proposed mapping-free localization framework. The results also show that using semantic information can help reduce localization errors in BIM.

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Section: Bim-based Pose Estimationmentioning

confidence: 99%

Semantic localization on BIM-generated maps using a 3D LiDAR sensor

Yin¹,

Lin²,

Yeoh³

2022

Preprint

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“…The first requirement assumes that feature instances (e.g., walls, columns) are linked to sensor representations (e.g. lines, corners) as described in Hendrikx et al (2021). In this work we use a system equipped with wheel encoder odometry and a conventional 2D planar laser scanning device.…”

Section: Requirements and Scopementioning

confidence: 99%

“…We specifically aim to use object-based prior representations that can be obtained from other sources as well (e.g. Hendrikx et al (2021)) in the form of vector maps. This makes or work more similar to Boniardi et al (2017); however, the authors do not focus on global localization in their work.…”

Section: Contributionsmentioning

confidence: 99%

“…Second, robots are expected to not only track their pose in a building, but to re obtain their location quickly after failure or reset, without an operator intervening. In prior work, we explored obtaining maps from industry standard building models which are already used to share building data (Hendrikx et al, 2021). In this work we focus on global localization, proposing a method that deals with the ambiguity and uncertainty in the environment on an association level.…”

Section: Introductionmentioning

confidence: 99%

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Local-To-Global Hypotheses for Robust Robot Localization

et al. 2022

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Many robust state-of-the-art localization methods rely on pose-space sample sets that are evaluated against individual sensor measurements. While these methods can work effectively, they often provide limited mechanisms to control the amount of hypotheses based on their similarity. Furthermore, they do not explicitly use associations to create or remove these hypotheses. We propose a global localization strategy that allows a mobile robot to localize using explicit symbolic associations with annotated geometric features. The feature measurements are first combined locally to form a consistent local feature map that is accurate in the vicinity of the robot. Based on this local map, an association tree is maintained that pairs local map features with global map features. The leaves of the tree represent distinct hypotheses on the data associations that allow for globally unmapped features appearing in the local map. We propose a registration step to check if an association hypothesis is supported. Our implementation considers a robot equipped with a 2D LiDAR and we compare the proposed method to a particle filter. We show that maintaining a smaller set of data association hypotheses results in better performance and explainability of the robot’s assumptions, as well as allowing more control over hypothesis bookkeeping. We provide experimental evaluations with a physical robot in a real environment using an annotated geometric building model that contains only the static part of the indoor scene. The result shows that our method outperforms a particle filter implementation in most cases by using fewer hypotheses with more descriptive power.

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“…Unlike our approach, their method requires a detailed map and manually assigning a semantic label to every grid cell. Hendrikx et al [13] utilize available building information model to extract both geometric and semantic information, and localize by matching 2D LiDARbased features corresponding to walls, corners and columns. While the automatic extraction of semantic and geometric maps from a BIM is promising, the approach is not suitable for global localization as it cannot overcome the challenges of a repetitively-structured environment.…”

Section: Related Workmentioning

confidence: 99%

Long-Term Localization using Semantic Cues in Floor Plan Maps

Zimmerman¹,

Guadagnino²,

Chen³

et al. 2022

Preprint

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Lifelong localization in a given map is an essential capability for autonomous service robots. In this paper, we consider the task of long-term localization in a changing indoor environment given sparse CAD floor plans. The commonly used pre-built maps from the robot sensors may increase the cost and time of deployment. Furthermore, their detailed nature requires that they are updated when significant changes occur. We address the difficulty of localization when the correspondence between the map and the observations is low due to the sparsity of the CAD map and the changing environment. To overcome both challenges, we propose to exploit semantic cues that are commonly present in human-oriented spaces. These semantic cues can be detected using RGB cameras by utilizing object detection, and are matched against an easy-to-update, abstract semantic map. The semantic information is integrated into a Monte Carlo localization framework using a particle filter that operates on 2D LiDAR scans and camera data. We provide a long-term localization solution and a semantic map format, for environments that undergo changes to their interior structure and detailed geometric maps are not available. We evaluate our localization framework on multiple challenging indoor scenarios in an office environment, taken weeks apart. The experiments suggest that our approach is robust to structural changes and can run on an onboard computer. We released the open source implementation 1 of our approach written in C++ together with a ROS wrapper.

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Connecting Semantic Building Information Models and Robotics: An application to 2D LiDAR-based localization

Cited by 19 publications

References 18 publications

Semantic localization on BIM-generated maps using a 3D LiDAR sensor

Semantic localization on BIM-generated maps using a 3D LiDAR sensor

Local-To-Global Hypotheses for Robust Robot Localization

Long-Term Localization using Semantic Cues in Floor Plan Maps

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