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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.11496 |
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| _version_ | 1866911800546557952 |
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| author | Nguyen, Thien-Minh Yuan, Shenghai Nguyen, Thien Hoang Yin, Pengyu Cao, Haozhi Xie, Lihua Wozniak, Maciej Jensfelt, Patric Thiel, Marko Ziegenbein, Justin Blunder, Noel |
| author_facet | Nguyen, Thien-Minh Yuan, Shenghai Nguyen, Thien Hoang Yin, Pengyu Cao, Haozhi Xie, Lihua Wozniak, Maciej Jensfelt, Patric Thiel, Marko Ziegenbein, Justin Blunder, Noel |
| contents | Perception plays a crucial role in various robot applications. However, existing well-annotated datasets are biased towards autonomous driving scenarios, while unlabelled SLAM datasets are quickly over-fitted, and often lack environment and domain variations. To expand the frontier of these fields, we introduce a comprehensive dataset named MCD (Multi-Campus Dataset), featuring a wide range of sensing modalities, high-accuracy ground truth, and diverse challenging environments across three Eurasian university campuses. MCD comprises both CCS (Classical Cylindrical Spinning) and NRE (Non-Repetitive Epicyclic) lidars, high-quality IMUs (Inertial Measurement Units), cameras, and UWB (Ultra-WideBand) sensors. Furthermore, in a pioneering effort, we introduce semantic annotations of 29 classes over 59k sparse NRE lidar scans across three domains, thus providing a novel challenge to existing semantic segmentation research upon this largely unexplored lidar modality. Finally, we propose, for the first time to the best of our knowledge, continuous-time ground truth based on optimization-based registration of lidar-inertial data on large survey-grade prior maps, which are also publicly released, each several times the size of existing ones. We conduct a rigorous evaluation of numerous state-of-the-art algorithms on MCD, report their performance, and highlight the challenges awaiting solutions from the research community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_11496 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MCD: Diverse Large-Scale Multi-Campus Dataset for Robot Perception Nguyen, Thien-Minh Yuan, Shenghai Nguyen, Thien Hoang Yin, Pengyu Cao, Haozhi Xie, Lihua Wozniak, Maciej Jensfelt, Patric Thiel, Marko Ziegenbein, Justin Blunder, Noel Robotics Artificial Intelligence Perception plays a crucial role in various robot applications. However, existing well-annotated datasets are biased towards autonomous driving scenarios, while unlabelled SLAM datasets are quickly over-fitted, and often lack environment and domain variations. To expand the frontier of these fields, we introduce a comprehensive dataset named MCD (Multi-Campus Dataset), featuring a wide range of sensing modalities, high-accuracy ground truth, and diverse challenging environments across three Eurasian university campuses. MCD comprises both CCS (Classical Cylindrical Spinning) and NRE (Non-Repetitive Epicyclic) lidars, high-quality IMUs (Inertial Measurement Units), cameras, and UWB (Ultra-WideBand) sensors. Furthermore, in a pioneering effort, we introduce semantic annotations of 29 classes over 59k sparse NRE lidar scans across three domains, thus providing a novel challenge to existing semantic segmentation research upon this largely unexplored lidar modality. Finally, we propose, for the first time to the best of our knowledge, continuous-time ground truth based on optimization-based registration of lidar-inertial data on large survey-grade prior maps, which are also publicly released, each several times the size of existing ones. We conduct a rigorous evaluation of numerous state-of-the-art algorithms on MCD, report their performance, and highlight the challenges awaiting solutions from the research community. |
| title | MCD: Diverse Large-Scale Multi-Campus Dataset for Robot Perception |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2403.11496 |