Paper Reading AI Learner

MRHaD: Mixed Reality-based Hand-Drawn Map Editing Interface for Mobile Robot Navigation

2025-04-01 09:34:02
Takumi Taki, Masato Kobayashi, Eduardo Iglesius, Naoya Chiba, Shizuka Shirai, Yuki Uranishi

Abstract

Mobile robot navigation systems are increasingly relied upon in dynamic and complex environments, yet they often struggle with map inaccuracies and the resulting inefficient path planning. This paper presents MRHaD, a Mixed Reality-based Hand-drawn Map Editing Interface that enables intuitive, real-time map modifications through natural hand gestures. By integrating the MR head-mounted display with the robotic navigation system, operators can directly create hand-drawn restricted zones (HRZ), thereby bridging the gap between 2D map representations and the real-world environment. Comparative experiments against conventional 2D editing methods demonstrate that MRHaD significantly improves editing efficiency, map accuracy, and overall usability, contributing to safer and more efficient mobile robot operations. The proposed approach provides a robust technical foundation for advancing human-robot collaboration and establishing innovative interaction models that enhance the hybrid future of robotics and human society. For additional material, please check: this https URL

Abstract (translated)

移动机器人导航系统在动态和复杂环境中越来越受到重视,但它们通常会因地图不准确而导致路径规划效率低下。本文提出了一种基于混合现实的手绘地图编辑界面MRHaD(Mixed Reality-based Hand-drawn Map Editing Interface),该界面允许用户通过自然手势进行直观、实时的地图修改。通过将混合现实头戴式显示器与机器人导航系统集成,操作员可以直接创建手绘限制区域(HRZ),从而弥合二维地图表示与真实世界环境之间的差距。 对比实验表明,MRHaD相比于传统的2D编辑方法,在提高编辑效率、地图准确性以及整体易用性方面表现显著。这些改进有助于实现更安全和高效的移动机器人作业。所提出的这种方法为推进人机协作提供了一个坚实的技术基础,并建立了一种创新的交互模型,以增强机器人技术与人类社会融合的未来。 欲了解更多信息,请访问:此链接 [请将“this https URL”替换为您实际提供的链接地址]

URL

https://arxiv.org/abs/2504.00580

PDF

https://arxiv.org/pdf/2504.00580.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model LLM Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Robot Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Time_Series Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot