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arXiv · 2610.09828

Enhancing Robotic Perception and Adaptability through Sensor Fusion and Origami-Inspired Designs

Namai Chandra·Jaison Jose·Kavi Arya·Shivaram Kalyanakrishnan
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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.

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Namai Chandra, Jaison Jose, Kavi Arya, Shivaram Kalyanakrishnan. 2026-10-07. Enhancing Robotic Perception and Adaptability through Sensor Fusion and Origami-Inspired Designs. https://arxiv.org/abs/2610.09828

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