تقرير
MCD: Diverse Large-Scale Multi-Campus Dataset for Robot Perception
| العنوان: | MCD: Diverse Large-Scale Multi-Campus Dataset for Robot Perception |
|---|---|
| المؤلفون: | Nguyen, Thien-Minh, Yuan, Shenghai, Nguyen, Thien Hoang, Yin, Pengyu, Cao, Haozhi, Xie, Lihua, Wozniak, Maciej, Jensfelt, Patric, Thiel, Marko, Ziegenbein, Justin, Blunder, Noel |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Robotics, Computer Science - Artificial Intelligence |
| الوصف: | 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. Comment: Accepted by The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2403.11496 |
| رقم الأكسشن: | edsarx.2403.11496 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |