2016/12/21 by Fei Xiaoxiao, Xiaoxiao, Fei, Kanji Tanaka +4
Computer Science · Social Sciences · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Geographic Information Systems Studies #Video Surveillance and Tracking Methods #cs.CV
paper · pdf · doi:10.48550/arxiv.1612.06933
Technical Report, 5 pages, 4 figures
arxiv created 2016/12/21 · openalex publication_date 2016/12/21 · arxiv updated 2016/12/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this study, we explore the use of deep convolutional neural networks (DCNNs) in visual place classification for robotic mapping and localization. An open question is how to partition the robot's workspace into places to maximize the performance (e.g., accuracy, precision, recall) of potential DCNN classifiers. This is a chicken and egg problem: If we had a well-trained DCNN classifier, it is rather easy to partition the robot's workspace into places, but the training of a DCNN classifier requires a set of pre-defined place classes. In this study, we address this problem and present several strategies for unsupervised discovery of place classes ("time cue," "location cue," "time-appearance cue," and "location-appearance cue"). We also evaluate the efficacy of the proposed methods using the publicly available University of Michigan North Campus Long-Term (NCLT) Dataset.