外观
论文arXiv 2610.0982821 天前
通过传感器融合与折纸启发式设计增强机器人感知与适应性
原文链接：Enhancing Robotic Perception and Adaptability through Sensor Fusion and Origami-Inspired Designs（arXiv 2610.09828 · 发布日期：2026-10-07）
Enhancing Robotic Perception and Adaptability through Sensor Fusion and Origami-Inspired Designs
针对载荷和成本受限的移动机器人几何感知，利用折纸轮调节LiDAR视角并融合IMU/RGB-D，在室内外验证深度覆盖提升。
核心要点
- 动机：在光照和表面纹理变化下，紧凑移动机器人需在载荷、成本受限时恢复场景几何。
- 方法：折纸启发式车轮改变底盘俯仰，使2D LiDAR扫过不同高度，并可保持指定视角。
- 融合节点用IMU补偿姿态，将LiDAR回波投影到RGB-D深度流，供RTAB-Map使用。
- 实验中无纹理室内走廊无效深度由21%降至11%，日照室外由48%降至18%。
验证范围与局限：验证限于一个低于300美元、低于2千克原型，在无纹理室内走廊和室外日照区域各传感器配置三次运行。
英文摘要 (Abstract)
Compact mobile robots must recover scene geometry under changing lighting and surface texture while working within tight payload and cost limits. We present a compact mobile robot that uses origami-inspired wheels for locomotion and active control of its sensing geometry. As the wheels move between terrain-adaptive configurations, the changing chassis pitch sweeps a 2D LiDAR through intermediate elevations; held wheel positions provide a chosen viewing angle. An IMU accounts for chassis attitude, and a fusion node projects LiDAR returns into the RGB-D depth stream supplied to RTAB-Map. The arrangement uses the wheel actuation already present on a sub-300 USD, sub-2 kg prototype to extend the scanner's viewing geometry. We assess depth fusion in a textureless indoor corridor and an outdoor sunlit area, with three runs per sensor configuration in each setting. Mean full-frame invalid-depth fractions fell from 21% to 11% indoors and from 48% to 18% outdoors. The prototype combines improved depth coverage with a continuously adjustable LiDAR viewpoint using the same actuation that reconfigures its wheels.
作者：Namai Chandra, Jaison Jose, Kavi Arya, Shivaram Kalyanakrishnan
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