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

2026-10-07

AI Summary

This study addresses the challenge of acquiring complete scene geometry for compact mobile robots constrained by limited payload capacity. We propose an active perception method utilizing origami-inspired wheels that repurpose drive actuators to dynamically adjust chassis posture and LiDAR scanning perspectives. By fusing IMU and RGB-D data to optimize RTAB-Map-based mapping, this approach overcomes the limitations of fixed sensor fields of view under low-cost and lightweight constraints. Experimental results demonstrate that the proposed method reduces the indoor invalid depth rate from 21% to 11% and the outdoor rate from 48% to 18%, significantly enhancing the completeness of depth coverage in 3D mapping across complex environments.

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.

Problem

• compact mobile robots
• scene geometry recovery
• sensor fusion
• changing lighting conditions
• depth coverage

Innovation

• Sensor Fusion
• Origami-Inspired Design
• Active Perception
• Mobile Robot
• Depth Completion