Journal
2022 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
Volume -, Issue -, Pages 5331-5338Publisher
IEEE
DOI: 10.1109/IROS47612.2022.9981318
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Funding
- Swiss National Science Foundation (SNSF) [PP00P2183720]
- NCCR Robotics, NCCR Digital Fabrication
- HILTI group
- ETH Mobility Initiative
- Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [EXC-2070 -390732324 - PhenoRob]
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Accurate maps are crucial for autonomous navigation robots, especially in handling large amounts of sensor data. Our proposed Voxfield framework can generate more accurate and complete maps online, with lower computational burden. Through a series of experiments, we demonstrate that our method outperforms existing techniques in map coverage, accuracy, and computational time.
Creating accurate maps of complex, unknown environments is of utmost importance for truly autonomous navigation robot. However, building these maps online is far from trivial, especially when dealing with large amounts of raw sensor readings on a computation and energy constrained mobile system, such as a small drone. While numerous approaches tackling this problem have emerged in recent years, the mapping accuracy is often sacrificed as systematic approximation errors are tolerated for efficiency's sake. Motivated by these challenges, we propose Voxfield, a mapping framework that can generate maps online with higher accuracy and lower computational burden than the state of the art. Built upon the novel formulation of non-projective truncated signed distance fields (TSDFs), our approach produces more accurate and complete maps, suitable for surface reconstruction. Additionally, it enables efficient generation of Euclidean signed distance fields (ESDFs), useful e.g., for path planning, that does not suffer from typical approximation errors. Through a series of experiments with public datasets, both real-world and synthetic, we demonstrate that our method beats the state of the art in map coverage, accuracy and computational time. Moreover, we show that Voxfield can be utilized as a back-end in recent multi-resolution mapping frameworks, producing high quality maps even in large-scale experiments. Finally, we validate our method by running it onboard a quadrotor, showing it can generate accurate ESDF maps usable for real-time path planning and obstacle avoidance.
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