发表机构
IIT Madras; e-Yantra, IIT Bombay（印度理工学院马德拉斯分校; e-Yantra，印度理工学院孟买分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种紧凑移动机器人，利用折纸轮子调节传感几何，通过传感器融合提升深度覆盖，在室内外环境中显著降低无效深度比例。
AI 中文摘要
紧凑型移动机器人必须在有限的载荷和成本限制下，在变化的照明和表面纹理环境中恢复场景几何信息。我们提出了一种紧凑型移动机器人，它采用折纸启发的轮子进行运动，并主动控制其传感几何结构。当轮子在适应地形的配置之间移动时，变化的底盘俯仰角使2D激光雷达扫过中间高度；保持的轮子位置提供了选定的视角。IMU负责底盘姿态，融合节点将激光雷达回波投影到提供给RTAB-Map的RGB-D深度流中。该布置利用亚300美元、亚2千克原型上已有的轮子驱动来扩展扫描仪的视角几何。我们在无纹理的室内走廊和室外阳光区域评估深度融合，每种传感器配置在每个环境中运行三次。平均全帧无效深度比例在室内从21%降至11%，在室外从48%降至18%。该原型结合了改进的深度覆盖和连续可调的激光雷达视角，使用与重新配置轮子相同的驱动。
英文摘要
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.
Comments11 pages, 11 figures, 3 tables
