Hilti SLAM Challenge 2023: Benchmarking Single + Multi-session SLAM across Sensor Constellations in Construction
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866914892410257408 |
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| author | Nair, Ashish Devadas Kindle, Julien Levchev, Plamen Scaramuzza, Davide |
| author_facet | Nair, Ashish Devadas Kindle, Julien Levchev, Plamen Scaramuzza, Davide |
| contents | Simultaneous Localization and Mapping systems are a key enabler for positioning in both handheld and robotic applications. The Hilti SLAM Challenges organized over the past years have been successful at benchmarking some of the world's best SLAM Systems with high accuracy. However, more capabilities of these systems are yet to be explored, such as platform agnosticism across varying sensor suites and multi-session SLAM. These factors indirectly serve as an indicator of robustness and ease of deployment in real-world applications. There exists no dataset plus benchmark combination publicly available, which considers these factors combined. The Hilti SLAM Challenge 2023 Dataset and Benchmark addresses this issue. Additionally, we propose a novel fiducial marker design for a pre-surveyed point on the ground to be observable from an off-the-shelf LiDAR mounted on a robot, and an algorithm to estimate its position at mm-level accuracy. Results from the challenge show an increase in overall participation, single-session SLAM systems getting increasingly accurate, successfully operating across varying sensor suites, but relatively few participants performing multi-session SLAM. Dataset URL: https://www.hilti-challenge.com/dataset-2023.html |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_09765 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hilti SLAM Challenge 2023: Benchmarking Single + Multi-session SLAM across Sensor Constellations in Construction Nair, Ashish Devadas Kindle, Julien Levchev, Plamen Scaramuzza, Davide Robotics Image and Video Processing Simultaneous Localization and Mapping systems are a key enabler for positioning in both handheld and robotic applications. The Hilti SLAM Challenges organized over the past years have been successful at benchmarking some of the world's best SLAM Systems with high accuracy. However, more capabilities of these systems are yet to be explored, such as platform agnosticism across varying sensor suites and multi-session SLAM. These factors indirectly serve as an indicator of robustness and ease of deployment in real-world applications. There exists no dataset plus benchmark combination publicly available, which considers these factors combined. The Hilti SLAM Challenge 2023 Dataset and Benchmark addresses this issue. Additionally, we propose a novel fiducial marker design for a pre-surveyed point on the ground to be observable from an off-the-shelf LiDAR mounted on a robot, and an algorithm to estimate its position at mm-level accuracy. Results from the challenge show an increase in overall participation, single-session SLAM systems getting increasingly accurate, successfully operating across varying sensor suites, but relatively few participants performing multi-session SLAM. Dataset URL: https://www.hilti-challenge.com/dataset-2023.html |
| title | Hilti SLAM Challenge 2023: Benchmarking Single + Multi-session SLAM across Sensor Constellations in Construction |
| topic | Robotics Image and Video Processing |
| url | https://arxiv.org/abs/2404.09765 |