Review of simultaneous localization and mapping (SLAM) for construction robotics applications

Yarovoi, Andrew; Cho, Yong Kwon

doi:10.1016/j.autcon.2024.105344

Cited by 10 publications

(3 citation statements)

References 37 publications

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“…For example, Kinect Fusion [12] can use the RGB-D Kinect camera to achieve instant 3D reconstruction of the environment through dense point cloud sampling and real-time tracking algorithms. Furthermore, algorithms from the LOAM series can also find applications in the Architecture, Engineering, and Construction (AEC) field [13].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Performance Evaluation between Visual SLAM and LiDAR SLAM for Mobile Robots: Theories and Experiments

Zhao,

Hong,

Huang

2024

Applied Sciences

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SLAM (Simultaneous Localization and Mapping), primarily relying on camera or LiDAR (Light Detection and Ranging) sensors, plays a crucial role in robotics for localization and environmental reconstruction. This paper assesses the performance of two leading methods, namely ORB-SLAM3 and SC-LeGO-LOAM, focusing on localization and mapping in both indoor and outdoor environments. The evaluation employs artificial and cost-effective datasets incorporating data from a 3D LiDAR and an RGB-D (color and depth) camera. A practical approach is introduced for calculating ground-truth trajectories and during benchmarking, reconstruction maps based on ground truth are established. To assess the performance, ATE and RPE are utilized to evaluate the accuracy of localization; standard deviation is employed to compare the stability during the localization process for different methods. While both algorithms exhibit satisfactory positioning accuracy, their performance is suboptimal in scenarios with inadequate textures. Furthermore, 3D reconstruction maps established by the two approaches are also provided for direct observation of their differences and the limitations encountered during map construction. Moreover, the research includes a comprehensive comparison of computational performance metrics, encompassing Central Processing Unit (CPU) utilization, memory usage, and an in-depth analysis. This evaluation revealed that Visual SLAM requires more CPU resources than LiDAR SLAM, primarily due to additional data storage requirements, emphasizing the impact of environmental factors on resource requirements. In conclusion, LiDAR SLAM is more suitable for the outdoors due to its comprehensive nature, while Visual SLAM excels indoors, compensating for sparse aspects in LiDAR SLAM. To facilitate further research, a technical guide was also provided for the researchers in related fields.

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Section: Introductionmentioning

confidence: 99%

Comprehensive Performance Evaluation between Visual SLAM and LiDAR SLAM for Mobile Robots: Theories and Experiments

Zhao,

Hong,

Huang

2024

Applied Sciences

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“…This technique, which holds a pivotal position in autonomous driving, augmented reality, and virtual reality, effectively mitigates the accumulation of errors in localization and map construction. By establishing robust constraints between the current frame and historical frames, loop-closure detection significantly enhances the practical utility of SLAM in autonomous navigation [ 2 ] and robotics applications [ 3 ]. Consequently, it facilitates the attainment of more precise and reliable spatial perception and navigation capabilities, thereby playing a pivotal role in ensuring the accuracy and efficiency of SLAM systems.…”

Section: Introductionmentioning

confidence: 99%

TS-LCD: Two-Stage Loop-Closure Detection Based on Heterogeneous Data Fusion

Jiang,

Wang,

You

et al. 2024

Sensors

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Loop-closure detection plays a pivotal role in simultaneous localization and mapping (SLAM). It serves to minimize cumulative errors and ensure the overall consistency of the generated map. This paper introduces a multi-sensor fusion-based loop-closure detection scheme (TS-LCD) to address the challenges of low robustness and inaccurate loop-closure detection encountered in single-sensor systems under varying lighting conditions and structurally similar environments. Our method comprises two innovative components: a timestamp synchronization method based on data processing and interpolation, and a two-order loop-closure detection scheme based on the fusion validation of visual and laser loops. Experimental results on the publicly available KITTI dataset reveal that the proposed method outperforms baseline algorithms, achieving a significant average reduction of 2.76% in the trajectory error (TE) and a notable decrease of 1.381 m per 100 m in the relative error (RE). Furthermore, it boosts loop-closure detection efficiency by an average of 15.5%, thereby effectively enhancing the positioning accuracy of odometry.

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“…Navigating in complex environments requires simultaneous localization and mapping (SLAM) approaches based on light detection and ranging (LiDAR) or visual sensors. [3][4][5][6][7][8] Visual SLAM is highly dependent on light conditions and texture but can offer fast mapping using a small and lightweight system or multiple cameras with high resolution and a high field of view. Using semantic interpretation can be used to improve SLAM operation in dynamic environments.…”

Section: Introductionmentioning

confidence: 99%

Autonomous measurement robotics for advanced mapping and inspection tasks in complex environments

Merkle,

Villinger,

Gangelhoff

et al. 2024

Optical Sensing and Detection VIII

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Mapping the environment is the basis for measurement tasks, planning processes, monitoring over time, and decision-making. Moreover, the inspection of engineering structures, roads and railways, water and wastewater, and energy infrastructure is essential to ensure safety and sustainability. For an automated mapping and inspection process, the use of robotic systems is indispensable. Due to the complexity of the environments of structures and the requirements for inspection, the automated operation requires real-time navigation, specific sensor solutions, and automated interpretation of the data. In this study, we present various concepts of autonomous measurement robotics for mapping and inspecting challenging environments and damage types. Robotic systems based on unmanned aerial vehicles, unmanned ground vehicles, and unmanned underwater vehicles are addressed for various scenarios including pipes, dams, bridges, tunnels, coastal areas from the air, and underwater structures. After identifying the demands and needs and presenting the state-of-the-art methods and systems and their identifying limitations, we present ways for an autonomous operation of robotic systems equipped with specific sensors for LiDAR-based subsurface damage detection of cavities or delaminations, underwater laser scanning, high-resolution bathymetry, and multispectral LiDAR for moisture detection. This includes real-time data interpretation, iterative inspection strategies, automated data interpretation, and the strategy for the integration of the human operator. In conclusion, this work gives an overview of existing methods and systems and their limitations for mapping and inspection tasks. Moreover, we show how advanced sensor systems can be used as part of autonomous robotic systems to overcome current limitations for high-quality mapping and inspection of challenging environments and structures.

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Review of simultaneous localization and mapping (SLAM) for construction robotics applications

Cited by 10 publications

References 37 publications

Comprehensive Performance Evaluation between Visual SLAM and LiDAR SLAM for Mobile Robots: Theories and Experiments

Comprehensive Performance Evaluation between Visual SLAM and LiDAR SLAM for Mobile Robots: Theories and Experiments

TS-LCD: Two-Stage Loop-Closure Detection Based on Heterogeneous Data Fusion

Autonomous measurement robotics for advanced mapping and inspection tasks in complex environments

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