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Found 15 papers, 0 papers with code
Detection and Classification of Pole-like Landmarks for Domain-invariant 3D Point Cloud Map Matching
In the former module, local pole map is constructed and its configuration is compared against a precomputed global pole map.
Deep SIMBAD: Active Landmark-based Self-localization Using Ranking -based Scene Descriptor
In this study, we consider an active self-localization task by an active observer and present a novel reinforcement learning (RL)-based next-best-view (NBV) planner.
Fault-Diagnosing SLAM for Varying Scale Change Detection
To address this issue, we reconsider the bag-of-words (BoW) image representation, by exploiting its recent advances in terms of self-localization and change detection.
Mining Minimal Map-Segments for Visual Place Classifiers
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 each class has good place-specific training images for a visual place classifier (VPC).
Place-specific Background Modeling Using Recursive Autoencoders
We introduce an efficient incremental recursive AE (rAE) training framework that recursively summarizes a large collection of background images into the AE set.
Long-Term Vehicle Localization by Recursive Knowledge Distillation
Most of the current state-of-the-art frameworks for cross-season visual place recognition (CS-VPR) focus on domain adaptation (DA) to a single specific season.
Scalable Change Retrieval Using Deep 3D Neural Codes
We present a novel scalable framework for image change detection (ICD) from an on-board 3D imagery system.
Detection-by-Localization: Maintenance-Free Change Object Detector
Recent researches demonstrate that self-localization performance is a very useful measure of likelihood-of-change (LoC) for change detection.
Use of Generative Adversarial Network for Cross-Domain Change Detection
In general, change detection aims to identify interesting changes between a given query image and a reference image of the same scene taken at a different time.
Zero-Shot Learning to Manage a Large Number of Place-Specific Compressive Change Classifiers
In our contribution, the proposed algorithm is applied and evaluated on a practical long-term cross-season change detection system that consists of a large number of place-specific object-level change classifiers.
Unsupervised Place Discovery for Place-Specific Change Classifier
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., support vector machine (SVM)) to predict changes from a robot's view image.
Change Detection under Global Viewpoint Uncertainty
Our approach, which utilizes a compact bag-of-words (BoW) scene model, makes several contributions to the problem: 1) Two kinds of prior information are extracted from the view sequence map and used for change detection.
Unsupervised Place Discovery for Visual Place Classification
In this study, we explore the use of deep convolutional neural networks (DCNNs) in visual place classification for robotic mapping and localization.
Self-localization from Images with Small Overlap
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 general self-localization scenarios.
Leveraging Image based Prior for Visual Place Recognition
In this study, we propose a novel scene descriptor for visual place recognition.
