Debra F. Laefer

Miftahur Rahman, Samuel Adebayo, Dorian A. Acevedo-Mejia et al.

The Intermeshed Steel Connection (ISC) system, when paired with robotic manipulators, can accelerate steel-frame assembly and improve worker safety by eliminating manual assembly. Dependable perception is one of the initial stages for ISC-aware robots. However, this is hampered by the absence of a dedicated image corpus, as collecting photographs on active construction sites is logistically difficult and raises safety and privacy concerns. In response, we introduce ISC-Perception, the first hybrid dataset expressly designed for ISC component detection. It blends procedurally rendered CAD images, game-engine photorealistic scenes, and a limited, curated set of real photographs, enabling fully automatic labelling of the synthetic portion. We explicitly account for all human effort to produce the dataset, including simulation engine and scene setup, asset preparation, post-processing scripts and quality checks; our total human time to generate a 10,000-image dataset was 30.5,h versus 166.7,h for manual labelling at 60,s per image (-81.7%). A manual pilot on a representative image with five instances of ISC members took 60,s (maximum 80,s), anchoring the manual baseline. Detectors trained on ISC-Perception achieved a mean Average Precision at IoU 0.50 of 0.756, substantially surpassing models trained on synthetic-only or photorealistic-only data. On a 1,200-frame bench test, we report mAP@0.50/mAP@[0.50:0.95] of 0.943/0.823. By bridging the data gap for construction-robotics perception, ISC-Perception facilitates rapid development of custom object detectors and is freely available for research and industrial use upon request.

V-H Cao, K-X Chu, Nhien-An Le-Khac et al.

Laser scanning (also known as Light Detection And Ranging) has been widely applied in various application. As part of that, aerial laser scanning (ALS) has been used to collect topographic data points for a large area, which triggers to million points to be acquired. Furthermore, today, with integrating full wareform (FWF) technology during ALS data acquisition, all return information of laser pulse is stored. Thus, ALS data are to be massive and complexity since the FWF of each laser pulse can be stored up to 256 samples and density of ALS data is also increasing significantly. Processing LiDAR data demands heavy operations and the traditional approaches require significant hardware and running time. On the other hand, researchers have recently proposed parallel approaches for analysing LiDAR data. These approaches are normally based on parallel architecture of target systems such as multi-core processors, GPU, etc. However, there is still missing efficient approaches/tools supporting the analysis of LiDAR data due to the lack of a deep study on both library tools and algorithms used in processing this data. In this paper, we present a comparative study of software libraries and algorithms to optimise the processing of LiDAR data. We also propose new method to improve this process with experiments on large LiDAR data. Finally, we discuss on a parallel solution of our approach where we integrate parallel computing in processing LiDAR data.