CAMAL: Context-Aware Multi-scale Attention framework for Lightweight Visual Place Recognition

In the last few years, Deep Convolutional Neural Networks (D-CNNs) have shown state-of-the-art performances for Visual Place Recognition (VPR). Their prestigious generalization power has played a vital role in identifying persistent image regions under changing conditions and viewpoints. However, against the computation intensive D-CNNs based VPR algorithms, lightweight VPR techniques are preferred for resource-constraints mobile robots. This paper presents a lightweight CNN-based VPR technique that captures multi-layer context-aware attentions robust under changing environment and viewpoints. Evaluation of challenging benchmark datasets reveals better performance at low memory and resources utilization over state-of-the-art contemporary VPR methodologies

A Holistic Visual Place Recognition Approach Using Lightweight CNNs for Significant ViewPoint and Appearance Changes

This article 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. Results on several benchmark datasets confirm an average boost of 13% in accuracy, and 12x average speedup relative to state-of-the-art methods

Visual Place Recognition under Severe Viewpoint and Appearance Changes

Over the last decade, the eagerness of the robotic and computer vision research communities unfolded extensive advancements in long-term robotic vision. Visual localization is the constituent of this active research domain; an ability of an object to correctly localize itself while mapping the environment simultaneously, technically termed as Simultaneous Localization and Mapping (SLAM). Visual Place Recognition (VPR), a core component of SLAM is a well-known paradigm. In layman terms, at a certain place/location within an environment, a robot needs to decide whether it’s the same place experienced before? Visual Place Recognition utilizing Convolutional Neural Networks (CNNs) has made a major contribution in the last few years. However, the image retrieval-based VPR becomes more challenging when the same places experience strong viewpoint and seasonal transitions. This thesis concentrates on improving the retrieval performance of VPR system, generally targeting the place correspondence. Despite the remarkable performances of state-of-the-art deep CNNs for VPR, the significant computation- and memory-overhead limit their practical deployment for resource constrained mobile robots. This thesis investigates the utility of shallow CNNs for power-efficient VPR applications. The proposed VPR frameworks focus on novel image regions that can contribute in recognizing places under dubious environment and viewpoint variations. Employing challenging place recognition benchmark datasets, this thesis further illustrates and evaluates the robustness of shallow CNN-based regional features against viewpoint and appearance changes coupled with dynamic instances, such as pedestrians, vehicles etc. Finally, the presented computation-efficient and light-weight VPR methodologies have shown boostup in matching performance in terms of Area under Precision-Recall curves (AUC-PR curves) over state-of-the-art deep neural network based place recognition and SLAM algorithms

CoHOG: A Light-Weight, Compute-Efficient, and Training-Free Visual Place Recognition Technique for Changing Environments

This letter presents a novel, compute-efficient and training-free approach based on Histogram-of-Oriented-Gradients (HOG) descriptor for achieving state-of-the-art performance-per-compute-unit in Visual Place Recognition (VPR). The inspiration for this approach (namely CoHOG) is based on the convolutional scanning and regions-based feature extraction employed by Convolutional Neural Networks (CNNs). By using image entropy to extract regions-of-interest (ROI) and regional-convolutional descriptor matching, our technique performs successful place recognition in changing environments. We use viewpoint- and appearance-variant public VPR datasets to report this matching performance, at lower RAM commitment, zero training requirements and 20 times lesser feature encoding time compared to state-of-the-art neural networks. We also discuss the image retrieval time of CoHOG and the effect of CoHOG's parametric variation on its place matching performance and encoding time

Data Efficient Visual Place Recognition Using Extremely JPEG-Compressed Images

Visual Place Recognition (VPR) is the ability of a robotic platform to correctly interpret visual stimuli from its on-board cameras in order to determine whether it is currently located in a previously visited place, despite different viewpoint, illumination and appearance changes. JPEG is a widely used image compression standard that is capable of significantly reducing the size of an image at the cost of image clarity. For applications where several robotic platforms are simultaneously deployed, the visual data gathered must be transmitted remotely between each robot. Hence, JPEG compression can be employed to drastically reduce the amount of data transmitted over a communication channel, as working with limited bandwidth for VPR can be proven to be a challenging task. However, the effects of JPEG compression on the performance of current VPR techniques have not been previously studied. For this reason, this paper presents an in-depth study of JPEG compression in VPR related scenarios. We use a selection of well-established VPR techniques on 8 datasets with various amounts of compression applied. We show that by introducing compression, the VPR performance is drastically reduced, especially in the higher spectrum of compression. To overcome the negative effects of JPEG compression on the VPR performance, we present a fine-tuned CNN which is optimized for JPEG compressed data and show that it performs more consistently with the image transformations detected in extremely compressed JPEG images.Comment: 8 pages, 8 figure

Visual Place Recognition for Autonomous Robots

Autonomous robotics has been the subject of great interest within the research community over the past few decades. Its applications are wide-spread, ranging from health-care to manufacturing, goods transportation to home deliveries, site-maintenance to construction, planetary explorations to rescue operations and many others, including but not limited to agriculture, defence, commerce, leisure and extreme environments. At the core of robot autonomy lies the problem of localisation, i.e, knowing where it is and within the robotics community, this problem is termed as place recognition. Place recognition using only visual input is termed as Visual Place Recognition (VPR) and refers to the ability of an autonomous system to recall a previously visited place using only visual input, under changing viewpoint, illumination and seasonal conditions, and given computational and storage constraints. This thesis is a collection of 4 inter-linked, mutually-relevant but branching-out topics within VPR: 1) What makes a place/image worthy for VPR?, 2) How to define a state-of-the-art in VPR?, 3) Do VPR techniques designed for ground-based platforms extend to aerial platforms? and 4) Can a handcrafted VPR technique outperform deep-learning-based VPR techniques? Each of these questions is a dedicated, peer-reviewed chapter in this thesis and the author attempts to answer these questions to the best of his abilities. The worthiness of a place essentially refers to the salience and distinctiveness of the content in the image of this place. This salience is modelled as a framework, namely memorable-maps, comprising of 3 conjoint criteria: a) Human-memorability of an image, 2) Staticity and 3) Information content. Because a large number of VPR techniques have been proposed over the past 10-15 years, and due to the variation of employed VPR datasets and metrics for evaluation, the correct state-of-the-art remains ambiguous. The author levels this playing field by deploying 10 contemporary techniques on a common platform and use the most challenging VPR datasets to provide a holistic performance comparison. This platform is then extended to aerial place recognition datasets to answer the 3rd question above. Finally, the author designs a novel, handcrafted, compute-efficient and training-free VPR technique that outperforms state-of-the-art VPR techniques on 5 different VPR datasets