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Found 103 papers, 43 papers with code
Enhancing Visual Place Recognition via Fast and Slow Adaptive Biasing in Event Cameras
Event cameras are increasingly popular in robotics due to their beneficial features, such as low latency, energy efficiency, and high dynamic range.
Multi-Technique Sequential Information Consistency For Dynamic Visual Place Recognition In Changing Environments
Visual place recognition (VPR) is an essential component of robot navigation and localization systems that allows them to identify a place using only image data.
Aggregating Multiple Bio-Inspired Image Region Classifiers For Effective And Lightweight Visual Place Recognition
Visual place recognition (VPR) enables autonomous systems to localize themselves within an environment using image information.
Design Space Exploration of Low-Bit Quantized Neural Networks for Visual Place Recognition
This has resulted in methods that use deep learning models too large to deploy on low powered edge devices.
Applications of Spiking Neural Networks in Visual Place Recognition
Lastly, we investigate the role of sequence matching in SNN-based VPR, a technique where consecutive images are used to refine place recognition.
FocusTune: Tuning Visual Localization through Focus-Guided Sampling
We propose FocusTune, a focus-guided sampling technique to improve the performance of visual localization algorithms.
Motion-Bias-Free Feature-Based SLAM
In this paper we show that SLAM commutativity, that is, consistency in trajectory estimates on forward and reverse traverses of the same route, is a significant issue for the state of the art.
Unsupervised Quality Prediction for Improved Single-Frame and Weighted Sequential Visual Place Recognition
While substantial progress has been made in the absolute performance of localization and Visual Place Recognition (VPR) techniques, it is becoming increasingly clear from translating these systems into applications that other capabilities like integrity and predictability are just as important, especially for safety- or operationally-critical autonomous systems.
Locking On: Leveraging Dynamic Vehicle-Imposed Motion Constraints to Improve Visual Localization
In this research, we propose a middle ground, demonstrated in the context of autonomous vehicles, using dynamic vehicles to provide limited pose constraint information in a 6-DoF frame-by-frame PnP-RANSAC localization pipeline.
DisPlacing Objects: Improving Dynamic Vehicle Detection via Visual Place Recognition under Adverse Conditions
In this work we investigate whether a prior map can be leveraged to aid in the detection of dynamic objects in a scene without the need for a 3D map or pixel-level map-query correspondences.
Patch-DrosoNet: Classifying Image Partitions With Fly-Inspired Models For Lightweight Visual Place Recognition
Visual place recognition (VPR) enables autonomous systems to localize themselves within an environment using image information.
An Evaluation and Ranking of Different Voting Schemes for Improved Visual Place Recognition
We aim to determine whether a single optimal voting scheme exists or, much like in other fields of research, the selection of a voting technique is relative to its application and environment.
Visual Place Recognition with Low-Resolution Images
Images incorporate a wealth of information from a robot's surroundings.
A-MuSIC: An Adaptive Ensemble System For Visual Place Recognition In Changing Environments
Visual place recognition (VPR) is an essential component of robot navigation and localization systems that allows them to identify a place using only image data.
Deep Declarative Dynamic Time Warping for End-to-End Learning of Alignment Paths
An interesting byproduct of this formulation is that DecDTW outputs the optimal warping path between two time series as opposed to a soft approximation, recoverable from Soft-DTW.
Visual Place Recognition: A Tutorial
It unifies the terminology of VPR and complements prior research in two important directions: 1) It provides a systematic introduction for newcomers to the field, covering topics such as the formulation of the VPR problem, a general-purpose algorithmic pipeline, an evaluation methodology for VPR approaches, and the major challenges for VPR and how they may be addressed.
A Complementarity-Based Switch-Fuse System for Improved Visual Place Recognition
The proposed, Switch-Fuse system, is an interesting way to combine both the robustness of switching VPR techniques based on complementarity and the force of fusing the carefully selected techniques to significantly improve performance.
Data-Efficient Sequence-Based Visual Place Recognition with Highly Compressed JPEG Images
The sequence length that enables 100% place matching performance is reported and an analysis of the amount of data required for each VPR technique to perform the transfer on the entire spectrum of JPEG compression is provided.
Residual Skill Policies: Learning an Adaptable Skill-based Action Space for Reinforcement Learning for Robotics
Furthermore, the downstream RL agent is limited to learning structurally similar tasks to those used to construct the skill space.
Boosting Performance of a Baseline Visual Place Recognition Technique by Predicting the Maximally Complementary Technique
One recent promising approach to the Visual Place Recognition (VPR) problem has been to fuse the place recognition estimates of multiple complementary VPR techniques using methods such as SRAL and multi-process fusion.
Merging Classification Predictions with Sequential Information for Lightweight Visual Place Recognition in Changing Environments
Low-overhead visual place recognition (VPR) is a highly active research topic.
Ensembles of Compact, Region-specific & Regularized Spiking Neural Networks for Scalable Place Recognition
We evaluate this new scalable modular system on benchmark localization datasets Nordland and Oxford RobotCar, with comparisons to standard techniques NetVLAD, DenseVLAD, and SAD, and a previous spiking neural network system.
Data Efficient Visual Place Recognition Using Extremely JPEG-Compressed Images
Moreover, this paper demonstrates how fine-tuning a CNN can be utilised as an optimisation method for JPEG compressed data to perform more consistently with the image transformations detected in extremely JPEG compressed images.
How Many Events do You Need? Event-based Visual Place Recognition Using Sparse But Varying Pixels
Event cameras continue to attract interest due to desirable characteristics such as high dynamic range, low latency, virtually no motion blur, and high energy efficiency.
Improving Worst Case Visual Localization Coverage via Place-specific Sub-selection in Multi-camera Systems
6-DoF visual localization systems utilize principled approaches rooted in 3D geometry to perform accurate camera pose estimation of images to a map.
Improving Road Segmentation in Challenging Domains Using Similar Place Priors
These approaches share one or more of three significant limitations: a reliance on large amounts of annotated training data that can be costly to obtain, both anticipation of and training data from the type of environmental conditions expected at inference time, and/or imagery captured from a previous visit to the location.
ReF -- Rotation Equivariant Features for Local Feature Matching
Sparse local feature matching is pivotal for many computer vision and robotics tasks.
SwitchHit: A Probabilistic, Complementarity-Based Switching System for Improved Visual Place Recognition in Changing Environments
This innovative use of multiple VPR techniques allow our system to be more efficient and robust than other combined VPR approaches employing brute force and running multiple VPR techniques at once.
Highly-Efficient Binary Neural Networks for Visual Place Recognition
In a typical BNN, the first convolution is not completely binarized for the sake of accuracy.
MultiRes-NetVLAD: Augmenting Place Recognition Training with Low-Resolution Imagery
Visual Place Recognition (VPR) is a crucial component of 6-DoF localization, visual SLAM and structure-from-motion pipelines, tasked to generate an initial list of place match hypotheses by matching global place descriptors.
Zero-Shot Uncertainty-Aware Deployment of Simulation Trained Policies on Real-World Robots
While deep reinforcement learning (RL) agents have demonstrated incredible potential in attaining dexterous behaviours for robotics, they tend to make errors when deployed in the real world due to mismatches between the training and execution environments.
Unsupervised Complementary-aware Multi-process Fusion for Visual Place Recognition
A recent approach to the Visual Place Recognition (VPR) problem has been to fuse the place recognition estimates of multiple complementary VPR techniques simultaneously.
PointCrack3D: Crack Detection in Unstructured Environments using a 3D-Point-Cloud-Based Deep Neural Network
The method was validated experimentally on a new large natural rock dataset, comprising coloured LIDAR point clouds spanning more than 900 m^2 and 412 individual cracks.
An Efficient and Scalable Collection of Fly-inspired Voting Units for Visual Place Recognition in Changing Environments
In this work, our goal is to provide an algorithm of extreme compactness and efficiency while achieving state-of-the-art robustness to appearance changes and small point-of-view variations.
A Benchmark Comparison of Visual Place Recognition Techniques for Resource-Constrained Embedded Platforms
(4) How does the performance of a high-end platform relate to an on-board low-end embedded platform for VPR?
Spiking Neural Networks for Visual Place Recognition via Weighted Neuronal Assignments
Spiking neural networks (SNNs) offer both compelling potential advantages, including energy efficiency and low latencies and challenges including the non-differentiable nature of event spikes.
Zero-Shot Day-Night Domain Adaptation with a Physics Prior
The traditional domain adaptation setting is to train on one domain and adapt to the target domain by exploiting unlabeled data samples from the test set.
Ranked #2 on
Image Retrieval
on 24/7 Tokyo
Bayesian Controller Fusion: Leveraging Control Priors in Deep Reinforcement Learning for Robotics
More importantly, given the risk-aversity of the control prior, BCF ensures safe exploration and deployment, where the control prior naturally dominates the action distribution in states unknown to the policy.
Probabilistic Appearance-Invariant Topometric Localization with New Place Awareness
Probabilistic state-estimation approaches offer a principled foundation for designing localization systems, because they naturally integrate sequences of imperfect motion and exteroceptive sensor data.
A Hierarchical Dual Model of Environment- and Place-Specific Utility for Visual Place Recognition
In this paper, we present a novel approach to deduce two key types of utility for VPR: the utility of visual cues `specific' to an environment, and to a particular place.
Ranked #1 on
Visual Place Recognition
on Berlin Kudamm
SeqNetVLAD vs PointNetVLAD: Image Sequence vs 3D Point Clouds for Day-Night Place Recognition
We compare a 3D point cloud based method (PointNetVLAD) with image sequence based methods (SeqNet and others) and showcase that image sequence based techniques approach, and can even surpass, the performance achieved by point cloud based methods for a given metric span.
Probabilistic Visual Place Recognition for Hierarchical Localization
In this letter, we propose two methods which adapt image retrieval techniques used for visual place recognition to the Bayesian state estimation formulation for localization.
Uncertainty for Identifying Open-Set Errors in Visual Object Detection
We also introduce a methodology for converting existing object detection datasets into specific open-set datasets to evaluate open-set performance in object detection.
RoRD: Rotation-Robust Descriptors and Orthographic Views for Local Feature Matching
The use of local detectors and descriptors in typical computer vision pipelines work well until variations in viewpoint and appearance change become extreme.
Where is your place, Visual Place Recognition?
Visual Place Recognition (VPR) is often characterized as being able to recognize the same place despite significant changes in appearance and viewpoint.
Sequential Place Learning: Heuristic-Free High-Performance Long-Term Place Recognition
Sequential matching using hand-crafted heuristics has been standard practice in route-based place recognition for enhancing pairwise similarity results for nearly a decade.
Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition
Visual Place Recognition is a challenging task for robotics and autonomous systems, which must deal with the twin problems of appearance and viewpoint change in an always changing world.
Ranked #1 on
Visual Localization
on RobotCar Seasons v2
Sequence-Based Filtering for Visual Route-Based Navigation: Analysing the Benefits, Trade-offs and Design Choices
This raises a number of interesting research questions: How does performance boost (due to sequential filtering) vary along the entire spectrum of single-frame-based matching methods?
Scene Retrieval for Contextual Visual Mapping
The second contribution is an algorithm `DMC' that combines our scene classification with distance and memorability for visual mapping.
SeqNet: Learning Descriptors for Sequence-based Hierarchical Place Recognition
Visual Place Recognition (VPR) is the task of matching current visual imagery from a camera to images stored in a reference map of the environment.
Improving Visual Place Recognition Performance by Maximising Complementarity
Visual place recognition (VPR) is the problem of recognising a previously visited location using visual information.
Semantics for Robotic Mapping, Perception and Interaction: A Survey
In robotics and related research fields, the study of understanding is often referred to as semantics, which dictates what does the world "mean" to a robot, and is strongly tied to the question of how to represent that meaning.
DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place Recognition
Sequence-based place recognition methods for all-weather navigation are well-known for producing state-of-the-art results under challenging day-night or summer-winter transitions.
Intelligent Reference Curation for Visual Place Recognition via Bayesian Selective Fusion
A key challenge in visual place recognition (VPR) is recognizing places despite drastic visual appearance changes due to factors such as time of day, season, weather or lighting conditions.
Early Bird: Loop Closures from Opposing Viewpoints for Perceptually-Aliased Indoor Environments
In particular, our integration of VPR with SLAM by leveraging the robustness of deep-learned features and our homography-based extreme viewpoint invariance significantly boosts the performance of VPR, feature correspondence, and pose graph submodules of the SLAM pipeline.
Binary Neural Networks for Memory-Efficient and Effective Visual Place Recognition in Changing Environments
To the best of our knowledge, this is the first attempt to propose binary neural networks for solving the visual place recognition problem effectively under changing conditions and with significantly reduced resource requirements.
ConvSequential-SLAM: A Sequence-based, Training-less Visual Place Recognition Technique for Changing Environments
In this paper, we present a new handcrafted VPR technique that achieves state-of-the-art place matching performance under challenging conditions.
Robot Perception enables Complex Navigation Behavior via Self-Supervised Learning
Learning visuomotor control policies in robotic systems is a fundamental problem when aiming for long-term behavioral autonomy.
Delta Descriptors: Change-Based Place Representation for Robust Visual Localization
Visual place recognition is challenging because there are so many factors that can cause the appearance of a place to change, from day-night cycles to seasonal change to atmospheric conditions.
Event-based visual place recognition with ensembles of temporal windows
Event cameras are bio-inspired sensors capable of providing a continuous stream of events with low latency and high dynamic range.
VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change
Visual Place Recognition (VPR) is the process of recognising a previously visited place using visual information, often under varying appearance conditions and viewpoint changes and with computational constraints.
Class Anchor Clustering: a Loss for Distance-based Open Set Recognition
We also show that our anchored class centres achieve higher open set performance than learnt class centres, particularly on object-based datasets and large numbers of training classes.
Multiplicative Controller Fusion: Leveraging Algorithmic Priors for Sample-efficient Reinforcement Learning and Safe Sim-To-Real Transfer
We present a novel approach to model-free reinforcement learning that can leverage existing sub-optimal solutions as an algorithmic prior during training and deployment.
MVP: Unified Motion and Visual Self-Supervised Learning for Large-Scale Robotic Navigation
Our experimental results, on traversals of the Oxford RobotCar dataset with no GPS data, show that MVP can achieve 53% and 93% navigation success rate using VO and RO, respectively, compared to 7% for a vision-only method.
Hierarchical Multi-Process Fusion for Visual Place Recognition
In this paper we present a novel, hierarchical localization system that explicitly benefits from three varying characteristics of localization techniques: the distribution of their localization hypotheses, their appearance- and viewpoint-invariant properties, and the resulting differences in where in an environment each system works well and fails.
Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations
Visual place recognition algorithms trade off three key characteristics: their storage footprint, their computational requirements, and their resultant performance, often expressed in terms of recall rate.
A Hybrid Compact Neural Architecture for Visual Place Recognition
State-of-the-art algorithms for visual place recognition, and related visual navigation systems, can be broadly split into two categories: computer-science-oriented models including deep learning or image retrieval-based techniques with minimal biological plausibility, and neuroscience-oriented dynamical networks that model temporal properties underlying spatial navigation in the brain.
CityLearn: Diverse Real-World Environments for Sample-Efficient Navigation Policy Learning
While deep reinforcement learning has shown success in solving these perception and decision-making problems in an end-to-end manner, these algorithms require large amounts of experience to learn navigation policies from high-dimensional data, which is generally impractical for real robots due to sample complexity.
Residual Reactive Navigation: Combining Classical and Learned Navigation Strategies For Deployment in Unknown Environments
In this work we focus on improving the efficiency and generalisation of learned navigation strategies when transferred from its training environment to previously unseen ones.
CAMAL: Context-Aware Multi-layer Attention framework for Lightweight Environment Invariant Visual Place Recognition
In the last few years, Deep Convolutional Neural Networks (D-CNNs) have shown state-of-the-art (SOTA) performance for Visual Place Recognition (VPR), a pivotal component of long-term intelligent robotic vision (vision-aware localization and navigation systems).
Visual Place Recognition for Aerial Robotics: Exploring Accuracy-Computation Trade-off for Local Image Descriptors
Visual Place Recognition (VPR) is a fundamental yet challenging task for small Unmanned Aerial Vehicle (UAV).
BTEL: A Binary Tree Encoding Approach for Visual Localization
Visual localization algorithms have achieved significant improvements in performance thanks to recent advances in camera technology and vision-based techniques.
Filter Early, Match Late: Improving Network-Based Visual Place Recognition
Our key innovation is to filter the feature maps in an early convolutional layer, but then continue to run the network and extract a feature vector using a later layer in the same network.
Are State-of-the-art Visual Place Recognition Techniques any Good for Aerial Robotics?
Visual Place Recognition (VPR) has seen significant advances at the frontiers of matching performance and computational superiority over the past few years.
Levelling the Playing Field: A Comprehensive Comparison of Visual Place Recognition Approaches under Changing Conditions
In recent years there has been significant improvement in the capability of Visual Place Recognition (VPR) methods, building on the success of both hand-crafted and learnt visual features, temporal filtering and usage of semantic scene information.
Multi-Process Fusion: Visual Place Recognition Using Multiple Image Processing Methods
In this paper we address these shortcomings with a novel "multi-sensor" fusion approach applied to multiple image processing methods for a single visual image stream, combined with a dynamic sequence matching length technique and an automatic processing method weighting scheme.
Robotics
Look No Deeper: Recognizing Places from Opposing Viewpoints under Varying Scene Appearance using Single-View Depth Estimation
Visual place recognition (VPR) - the act of recognizing a familiar visual place - becomes difficult when there is extreme environmental appearance change or viewpoint change.
Robotics
SASSE: Scalable and Adaptable 6-DOF Pose Estimation
In this paper, we present a novel 6-DOF localization system that for the first time simultaneously achieves all the three characteristics: significantly sub-linear storage growth, agnosticism to image descriptors, and customizability to available storage and computational resources.
Memorable Maps: A Framework for Re-defining Places in Visual Place Recognition
This paper presents a cognition-inspired agnostic framework for building a map for Visual Place Recognition.
A Holistic Visual Place Recognition Approach using Lightweight CNNs for Significant ViewPoint and Appearance Changes
This paper presents a lightweight visual place recognition approach, capable of achieving high performance with low computational cost, and feasible for mobile robotics under significant viewpoint and appearance changes.
A Binary Optimization Approach for Constrained K-Means Clustering
This approach allows us to solve constrained K-Means where multiple types of constraints can be simultaneously enforced.
Large scale visual place recognition with sub-linear storage growth
Robotic and animal mapping systems share many of the same objectives and challenges, but differ in one key aspect: where much of the research in robotic mapping has focused on solving the data association problem, the grid cell neurons underlying maps in the mammalian brain appear to intentionally break data association by encoding many locations with a single grid cell neuron.
Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detection
There has been a recent emergence of sampling-based techniques for estimating epistemic uncertainty in deep neural networks.
The Limits and Potentials of Deep Learning for Robotics
In this paper we discuss a number of robotics-specific learning, reasoning, and embodiment challenges for deep learning.
Robotics
LoST? Appearance-Invariant Place Recognition for Opposite Viewpoints using Visual Semantics
Human visual scene understanding is so remarkable that we are able to recognize a revisited place when entering it from the opposite direction it was first visited, even in the presence of extreme variations in appearance.
QuadricSLAM: Dual Quadrics from Object Detections as Landmarks in Object-oriented SLAM
In this paper, we use 2D object detections from multiple views to simultaneously estimate a 3D quadric surface for each object and localize the camera position.
Robotics
OpenSeqSLAM2.0: An Open Source Toolbox for Visual Place Recognition Under Changing Conditions
Visually recognising a traversed route - regardless of whether seen during the day or night, in clear or inclement conditions, or in summer or winter - is an important capability for navigating robots.
One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay
Recently, model-free reinforcement learning algorithms have been shown to solve challenging problems by learning from extensive interaction with the environment.
Addressing Challenging Place Recognition Tasks using Generative Adversarial Networks
In the process, meaningful feature spaces are learned for each domain, the distances in which can be used for the task of place recognition.
Robotics
Adversarial Discriminative Sim-to-real Transfer of Visuo-motor Policies
Policies can be transferred to real environments with only 93 labelled and 186 unlabelled real images.
Multi-Modal Trip Hazard Affordance Detection On Construction Sites
Trip hazards are a significant contributor to accidents on construction and manufacturing sites, where over a third of Australian workplace injuries occur [1].
Tuning Modular Networks with Weighted Losses for Hand-Eye Coordination
This paper introduces an end-to-end fine-tuning method to improve hand-eye coordination in modular deep visuo-motor policies (modular networks) where each module is trained independently.
Action Recognition: From Static Datasets to Moving Robots
Deep learning models have achieved state-of-the- art performance in recognizing human activities, but often rely on utilizing background cues present in typical computer vision datasets that predominantly have a stationary camera.
Deep Learning Features at Scale for Visual Place Recognition
The success of deep learning techniques in the computer vision domain has triggered a range of initial investigations into their utility for visual place recognition, all using generic features from networks that were trained for other types of recognition tasks.
3D tracking of water hazards with polarized stereo cameras
In this paper, we present a novel stereo-polarization system for detecting and tracking water hazards based on polarization and color variation of reflected light, with consideration of the effect of polarized light from sky as function of reflection and azimuth angles.
Modular Deep Q Networks for Sim-to-real Transfer of Visuo-motor Policies
While deep learning has had significant successes in computer vision thanks to the abundance of visual data, collecting sufficiently large real-world datasets for robot learning can be costly.
Meaningful Maps With Object-Oriented Semantic Mapping
For intelligent robots to interact in meaningful ways with their environment, they must understand both the geometric and semantic properties of the scene surrounding them.
Robotics
2D Visual Place Recognition for Domestic Service Robots at Night
In this paper we present a passive and potentially cheap vision-based solution to 2D localization at night that combines easily obtainable day-time maps with low resolution contrast-normalized image matching algorithms, image sequence-based matching in two-dimensions, place match interpolation and recent advances in conventional low light camera technology.
Evaluation of Object Detection Proposals Under Condition Variations
Object detection is a fundamental task in many computer vision applications, therefore the importance of evaluating the quality of object detection is well acknowledged in this domain.
Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control
This paper introduces a machine learning based system for controlling a robotic manipulator with visual perception only.
Place Recognition with Event-based Cameras and a Neural Implementation of SeqSLAM
Event-based cameras offer much potential to the fields of robotics and computer vision, in part due to their large dynamic range and extremely high "frame rates".
On the Performance of ConvNet Features for Place Recognition
Computer vision datasets are very different in character to robotic camera data, real-time performance is essential, and performance priorities can be different.
Convolutional Neural Network-based Place Recognition
Recently Convolutional Neural Networks (CNNs) have been shown to achieve state-of-the-art performance on various classification tasks.
