13 citations · 27 across the 32 of their papers we have counts for
26 papers · 1 filter
Lifelong Change Detection: Continuous Domain Adaptation for Small Object Change Detection in Every Robot Navigation
Koji Takeda, Kanji Tanaka, Yoshimasa Nakamura
The recently emerging research area in robotics, ground view change detection, suffers from its ill-posed-ness because of visual uncertainty combined with complex nonlinear perspec…
PartSLAM: Unsupervised Part-based Scene Modeling for Fast Succinct Map Matching
Shogo Hanada, Kanji Tanaka
In this paper, we explore the challenging 1-to-N map matching problem, which exploits a compact description of map data, to improve the scalability of map matching techniques used…
Detection and Classification of Pole-like Landmarks for Domain-invariant 3D Point Cloud Map Matching
Sun Yifei, Li Dingrui, Ye Minying +1
In 3D point cloud-based visual self-localization, pole landmarks have a great potential as landmarks for accurate and reliable localization due to their long-term stability under s…
A Multi-modal Approach to Single-modal Visual Place Classification
Tomoya Iwasaki, Kanji Tanaka, Kenta Tsukahara
Visual place classification from a first-person-view monocular RGB image is a fundamental problem in long-term robot navigation. A difficulty arises from the fact that RGB image cl…
Active Semantic Localization with Graph Neural Embedding
Mitsuki Yoshida, Kanji Tanaka, Ryogo Yamamoto +1
Semantic localization, i.e., robot self-localization with semantic image modality, is critical in recently emerging embodied AI applications (e.g., point-goal navigation, object-go…
Compressive Self-localization Using Relative Attribute Embedding
Ryogo Yamamoto, Kanji Tanaka
The use of relative attribute (e.g., beautiful, safe, convenient) -based image embeddings in visual place recognition, as a domain-adaptive compact image descriptor that is orthogo…
Domain Invariant Siamese Attention Mask for Small Object Change Detection via Everyday Indoor Robot Navigation
Koji Takeda, Kanji Tanaka, Yoshimasa Nakamura
The problem of image change detection via everyday indoor robot navigation is explored from a novel perspective of the self-attention technique. Detecting semantically non-distinct…
Exploring Self-Attention for Visual Intersection Classification
Haruki Nakata, Kanji Tanaka, Koji Takeda
In robot vision, self-attention has recently emerged as a technique for capturing non-local contexts. In this study, we introduced a self-attention mechanism into the intersection…
Open-World Distributed Robot Self-Localization with Transferable Visual Vocabulary and Both Absolute and Relative Features
Mitsuki Yoshida, Ryogo Yamamoto, Daiki Iwata +1
Visual robot self-localization is a fundamental problem in visual robot navigation and has been studied across various problem settings, including monocular and sequential localiza…
TaylorMade VDD: Domain-adaptive Visual Defect Detector for High-mix Low-volume Production of Non-convex Cylindrical Metal Objects
Kyosuke Tashiro, Koji Takeda, Kanji Tanaka +1
Visual defect detection (VDD) for high-mix low-volume production of non-convex metal objects, such as high-pressure cylindrical piping joint parts (VDD-HPPPs), is challenging becau…
Domain-invariant NBV Planner for Active Cross-domain Self-localization
Kanji Tanaka
Pole-like landmark has received increasing attention as a domain-invariant visual cue for visual robot self-localization across domains (e.g., seasons, times of day, weathers). How…
Dark Reciprocal-Rank: Boosting Graph-Convolutional Self-Localization Network via Teacher-to-student Knowledge Transfer
Koji Takeda, Kanji Tanaka
In visual robot self-localization, graph-based scene representation and matching have recently attracted research interest as robust and discriminative methods for selflocalization…
Fault-Diagnosing SLAM for Varying Scale Change Detection
Sugimoto Takuma, Yamaguchi Kousuke, Tanaka Kanji
In this paper, we present a new fault diagnosis (FD) -based approach for detection of imagery changes that can detect significant changes as inconsistencies between different sub-m…
Mining Minimal Map-Segments for Visual Place Classifiers
Tanaka Kanji
In visual place recognition (VPR), map segmentation (MS) is a preprocessing technique used to partition a given view-sequence map into place classes (i.e., map segments) so that ea…
Place-specific Background Modeling Using Recursive Autoencoders
Yamaguchi Kousuke, Tanaka Kanji, Sugimoto Takuma +2
Image change detection (ICD) to detect changed objects in front of a vehicle with respect to a place-specific background model using an on-board monocular vision system is a fundam…
Use of First and Third Person Views for Deep Intersection Classification
Koji Takeda, Kanji Tanaka
We explore the problem of intersection classification using monocular on-board passive vision, with the goal of classifying traffic scenes with respect to road topology. We divide…
Use of Generative Adversarial Network for Cross-Domain Change Detection
Yamaguchi Kousuke, Tanaka Kanji, Sugimoto Takuma
This paper addresses the problem of cross-domain change detection from a novel perspective of image-to-image translation. In general, change detection aims to identify interesting…
Long-Term Ensemble Learning of Visual Place Classifiers
Xiaoxiao Fei, Kanji Tanaka, Yichu Fang +1
This paper addresses the problem of cross-season visual place classification (VPC) from a novel perspective of long-term map learning. Our goal is to enable transfer learning effic…
Unsupervised Place Discovery for Place-Specific Change Classifier
Fei Xiaoxiao, Tanaka Kanji
In this study, we address the problem of supervised change detection for robotic map learning applications, in which the aim is to train a place-specific change classifier (e.g., s…
Unsupervised Place Discovery for Visual Place Classification
Fei Xiaoxiao, Tanaka Kanji, Inamoto Kouya
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 part…
Multi-Model Hypothesize-and-Verify Approach for Incremental Loop Closure Verification
Kanji Tanaka
Loop closure detection, which is the task of identifying locations revisited by a robot in a sequence of odometry and perceptual observations, is typically formulated as a visual p…
Compressive Change Retrieval for Moving Object Detection
Tomoya Murase, Kanji Tanaka
Change detection, or anomaly detection, from street-view images acquired by an autonomous robot at multiple different times, is a major problem in robotic mapping and autonomous dr…
Self-localization from Images with Small Overlap
Tanaka Kanji
With the recent success of visual features from deep convolutional neural networks (DCNN) in visual robot self-localization, it has become important and practical to address more g…
Self-localization Using Visual Experience Across Domains
Taisho Tsukamoto, Kanji Tanaka
In this study, we aim to solve the single-view robot self-localization problem by using visual experience across domains. Although the bag-of-words method constitutes a popular app…