Max Robotics — Global Service Robot Directory + US Certification

About

Ruixu Liu is a researcher focused on advancing robotic perception and autonomous navigation through efficient 3D scene understanding and simultaneous localization and mapping (SLAM). His work centers on enabling mobile robots to operate with high autonomy in indoor environments by addressing critical challenges in real-time 3D reconstruction, change detection, and power-efficient computation. Liu’s most impactful contribution, “Evaluating the Power Efficiency of Visual SLAM on Embedded GPU Systems” (21 citations), systematically benchmarks GPU-accelerated ORB-SLAM2 on embedded platforms, providing essential insights for deploying SLAM on battery-constrained robots. He further developed methodologies for 3D indoor scene reconstruction and change detection, including a staggered voxels model that supports robust robotic sensing and navigation under varying conditions. Liu has also pioneered the fusion of RGB-D and inertial data using recurrent and convolutional neural networks to improve SLAM accuracy, and proposed a real-time surface optimization system for dense 3D point cloud creation with loop-closure detection. His cumulative work, spanning over 38 citations, directly contributes to more reliable, energy-aware, and perceptive autonomous systems, making his research valuable for engineers and scientists developing next-generation service and field robots.