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Found 25 papers, 0 papers with code
Zero-shot Degree of Ill-posedness Estimation for Active Small Object Change Detection
To regularize this problem, we apply the conceptof self-supervised learning to achieve efficient DoI estimationscheme and investigate its generalization to diverse datasets. Specifically, we tackle the challenging issue of obtaining self-supervision cues for semantically non-distinctive unseen smallobjects and show that novel "oversegmentation cues" from openvocabulary semantic segmentation can be effectively exploited. When applied to diverse real datasets, the proposed DoI modelcan boost state-of-the-art change detection models, and it showsstable and consistent improvements when evaluated on real-world datasets.
Training Self-localization Models for Unseen Unfamiliar Places via Teacher-to-Student Data-Free Knowledge Transfer
Rather than relying on the availability of private data of teachers as in existing methods, we propose to exploit an assumption that holds universally in self-localization tasks: "The teacher model is a self-localization system" and to reuse the self-localization system of a teacher as a sole accessible communication channel.
Recursive Distillation for Open-Set Distributed Robot Localization
A typical assumption in state-of-the-art self-localization models is that an annotated training dataset is available for the target workspace.
Cross-view Self-localization from Synthesized Scene-graphs
Cross-view self-localization is a challenging scenario of visual place recognition in which database images are provided from sparse viewpoints.
Walking = Traversable? : Traversability Prediction via Multiple Human Object Tracking under Occlusion
The emerging ``Floor plan from human trails (PfH)" technique has great potential for improving indoor robot navigation by predicting the traversability of occluded floors.
Lifelong Change Detection: Continuous Domain Adaptation for Small Object Change Detection in Every Robot Navigation
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 perspective projection.
PartSLAM: Unsupervised Part-based Scene Modeling for Fast Succinct Map Matching
The tasks also include an online map matching attempt to efficiently find correspondence between the part-based maps.
A Multi-modal Approach to Single-modal Visual Place Classification
Visual place classification from a first-person-view monocular RGB image is a fundamental problem in long-term robot navigation.
Active Semantic Localization with Graph Neural Embedding
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-goal navigation, vision language navigation) and topological mapping applications (e. g., graph neural SLAM, ego-centric topological map).
Compressive Self-localization Using Relative Attribute Embedding
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 orthogonal to the typical approach of absolute attribute (e. g., color, shape, texture) -based image embeddings, is explored in this paper.
Active Domain-Invariant Self-Localization Using Ego-Centric and World-Centric Maps
The OLC is available at the output layer of the CNN model and aims to estimate the state of the robot (e. g., the robot viewpoint) with respect to the world-centric view coordinate system.
Domain Invariant Siamese Attention Mask for Small Object Change Detection via Everyday Indoor Robot Navigation
Experiments, in which an indoor robot aims to detect visually small changes in everyday navigation, demonstrate that our attention technique significantly boosts the state-of-the-art image change detection model.
Exploring Self-Attention for Visual Intersection Classification
In this study, we introduced a self-attention mechanism into the intersection recognition system as a method to capture the non-local contexts behind the scenes.
Highly Compressive Visual Self-localization Using Sequential Semantic Scene Graph and Graph Convolutional Neural Network
In this paper, we address the problem of image sequence-based self-localization from a new highly compressive scene representation called sequential semantic scene graph (S3G).
TaylorMade VDD: Domain-adaptive Visual Defect Detector for High-mix Low-volume Production of Non-convex Cylindrical Metal Objects
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 because subtle difference in domain (e. g., metal objects, imaging device, viewpoints, lighting) significantly affects the specular reflection characteristics of individual metal object types.
Domain-invariant NBV Planner for Active Cross-domain Self-localization
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).
Dark Reciprocal-Rank: Boosting Graph-Convolutional Self-Localization Network via Teacher-to-student Knowledge Transfer
In visual robot self-localization, graph-based scene representation and matching have recently attracted research interest as robust and discriminative methods for selflocalization.
Use of First and Third Person Views for Deep Intersection Classification
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.
Long-Term Ensemble Learning of Visual Place Classifiers
This paper addresses the problem of cross-season visual place classification (VPC) from a novel perspective of long-term map learning.
Multi-Model Hypothesize-and-Verify Approach for Incremental Loop Closure Verification
Furthermore, we consider the general incremental setting of loop closure detection, in which the system must update both the set of VPR constraints and that of loop closure hypotheses when new constraints or hypotheses arrive during robot navigation.
Compressive Change Retrieval for Moving Object Detection
Formulation as an image comparison task, which operates on a given pair of query and reference images is common to many existing approaches to this problem.
Incremental Loop Closure Verification by Guided Sampling
Loop closure detection, the task of identifying locations revisited by a robot in a sequence of odometry and perceptual observations, is typically formulated as a combination of two subtasks: (1) bag-of-words image retrieval and (2) post-verification using RANSAC geometric verification.
Self-localization Using Visual Experience Across Domains
In this study, we aim to solve the single-view robot self-localization problem by using visual experience across domains.
Discriminative Map Retrieval Using View-Dependent Map Descriptor
The main contribution of this paper is an extension of the bag-of-words map retrieval method to enable the use of spatial information from local features.
Incremental RANSAC for Online Relocation in Large Dynamic Environments
Vehicle relocation is the problem in which a mobile robot has to estimate the self-position with respect to an a priori map of landmarks using the perception and the motion measurements without using any knowledge of the initial self-position.
