RO-LOAM: 3D Reference Object-based Trajectory and Map Optimization in LiDAR Odometry and Mapping

Oelsch, Martin; Karimi, Mojtaba; Steinbach, Eckehard

doi:10.1109/lra.2022.3177846

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…Nonetheless, the method is not restricted to Manhattanworld environments with enclosed rooms, like in [22], nor does it require manual input of the robot's initial position in the map like the one proposed in [19].…”

Section: Discussionmentioning

confidence: 99%

“…[18] propose Reference-LOAM (R-LOAM), a method that leverages a joint optimization incorporating point and mesh features for 6 degrees of freedom (DoF) Unmanned Aerial Vehicle (UAV) localization. Subsequently, in [19], they improved their method with pose-graph optimization to reduce drift even when the reference object is not visible.…”

Section: Bim-based 3d Lidar Localization and Mappingmentioning

confidence: 99%

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BIM-SLAM: Integrating BIM Models in Multi-session SLAM for Lifelong Mapping using 3D LiDAR

Torres¹,

Braun²,

Borrmann³

2023

Proceedings of the 40th International Symposium on Automation and Robotics in Construction

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While 3D Light Detection and Ranging (LiDAR) sensor technology is becoming more advanced and cheaper every day, the growth of digitalization in the Architecture, Engineering and Construction (AEC) industry contributes to the fact that 3D building information models (BIM models) are now available for a large part of the built environment. These two facts open the question of how 3D models can support 3D LiDAR long-term Simultaneous Localization and Mapping (SLAM) in indoor, Global Positioning System (GPS)-denied environments. This paper proposes a methodology that leverages BIM models to create an updated map of indoor environments with sequential LiDAR measurements. Session data (pose graph-based map and descriptors) are initially generated from BIM models. Then, real-world data is aligned with the session data from the model using multi-session anchoring while minimizing the drift on the real-world data. Finally, the new elements not present in the BIM model are identified, grouped, and reconstructed in a surface representation, allowing a better visualization next to the BIM model. The framework enables the creation of a coherent map aligned with the BIM model that does not require prior knowledge of the initial pose of the robot, and it does not need to be inside the map.

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Section: Discussionmentioning

confidence: 99%

Section: Bim-based 3d Lidar Localization and Mappingmentioning

confidence: 99%

BIM-SLAM: Integrating BIM Models in Multi-session SLAM for Lifelong Mapping using 3D LiDAR

Torres¹,

Braun²,

Borrmann³

2023

Proceedings of the 40th International Symposium on Automation and Robotics in Construction

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“…It leverages planar features from ground points and edge features from segmented points to incrementally determine a 6 degree-of-freedom (DOF) transformation. R-LOAM [101] and RO-LOAM [102] optimize the robot's trajectory by incorporating mesh features derived from the 3D triangular mesh of a reference object with a known global coordinate location. Plane features, prevalent in everyday environments, have garnered significant attention as they can be easily extracted from the LiDAR point cloud.…”

Section: Feature-based Matchingmentioning

confidence: 99%

LiDAR odometry survey: recent advancements and remaining challenges

Lee,

Jung,

Yang

et al. 2024

Intel Serv Robotics

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Odometry is crucial for robot navigation, particularly in situations where global positioning methods like global positioning system are unavailable. The main goal of odometry is to predict the robot’s motion and accurately determine its current location. Various sensors, such as wheel encoder, inertial measurement unit (IMU), camera, radar, and Light Detection and Ranging (LiDAR), are used for odometry in robotics. LiDAR, in particular, has gained attention for its ability to provide rich three-dimensional (3D) data and immunity to light variations. This survey aims to examine advancements in LiDAR odometry thoroughly. We start by exploring LiDAR technology and then scrutinize LiDAR odometry works, categorizing them based on their sensor integration approaches. These approaches include methods relying solely on LiDAR, those combining LiDAR with IMU, strategies involving multiple LiDARs, and methods fusing LiDAR with other sensor modalities. In conclusion, we address existing challenges and outline potential future directions in LiDAR odometry. Additionally, we analyze public datasets and evaluation methods for LiDAR odometry. To our knowledge, this survey is the first comprehensive exploration of LiDAR odometry.

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SLAM2REF: advancing long-term mapping with 3D LiDAR and reference map integration for precise 6-DoF trajectory estimation and map extension

Vega-Torres,

Braun,

Borrmann

2024

Constr Robot

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This paper presents a pioneering solution to the task of integrating mobile 3D LiDAR and inertial measurement unit (IMU) data with existing building information models or point clouds, which is crucial for achieving precise long-term localization and mapping in indoor, GPS-denied environments. Our proposed framework, SLAM2REF, introduces a novel approach for automatic alignment and map extension utilizing reference 3D maps. The methodology is supported by a sophisticated multi-session anchoring technique, which integrates novel descriptors and registration methodologies. Real-world experiments reveal the framework’s remarkable robustness and accuracy, surpassing current state-of-the-art methods. Our open-source framework’s significance lies in its contribution to resilient map data management, enhancing processes across diverse sectors such as construction site monitoring, emergency response, disaster management, and others, where fast-updated digital 3D maps contribute to better decision-making and productivity. Moreover, it offers advancements in localization and mapping research. Link to the repository: https://github.com/MigVega/SLAM2REF, Data: https://doi.org/10.14459/2024mp1743877.

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RO-LOAM: 3D Reference Object-based Trajectory and Map Optimization in LiDAR Odometry and Mapping

Cited by 5 publications

References 25 publications

BIM-SLAM: Integrating BIM Models in Multi-session SLAM for Lifelong Mapping using 3D LiDAR

BIM-SLAM: Integrating BIM Models in Multi-session SLAM for Lifelong Mapping using 3D LiDAR

LiDAR odometry survey: recent advancements and remaining challenges

SLAM2REF: advancing long-term mapping with 3D LiDAR and reference map integration for precise 6-DoF trajectory estimation and map extension

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