2024/10/08 by Minsoo Kim, Obin Kwon, Kim, Minsoo +5
Computer Science · #Advanced Image and Video Retrieval Techniques #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Multimodal Machine Learning Applications #Video Analysis and Summarization #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2410.05621
openalex publication_date 2024/10/08 · openalex created_date 2024/10/12 · openalex updated_date 2026/07/28
We propose a novel visual localization and navigation framework for real-world environments directly integrating observed visual information into the bird-eye-view map. While the renderable neural radiance map (RNR-Map) shows considerable promise in simulated settings, its deployment in real-world scenarios poses undiscovered challenges. RNR-Map utilizes projections of multiple vectors into a single latent code, resulting in information loss under suboptimal conditions. To address such issues, our enhanced RNR-Map for real-world robots, RNR-Map++, incorporates strategies to mitigate information loss, such as a weighted map and positional encoding. For robust real-time localization, we integrate a particle filter into the correlation-based localization framework using RNRMap++ without a rendering procedure. Consequently, we establish a real-world robot system for visual navigation utilizing RNR-Map++, which we call "RNR-Nav." Experimental results demonstrate that the proposed methods significantly enhance rendering quality and localization robustness compared to previous approaches. In real-world navigation tasks, RNR-Nav achieves a success rate of 84.4%, marking a 68.8% enhancement over the methods of the original RNR-Map paper.