Low-level Geometric Features Research Articles

Robust loop closure detection and relocalization with semantic-line graph matching constraints in indoor environments

Loop closure detection (LCD) plays an essential role in the Simultaneous Localization and Mapping (SLAM) process, effectively reducing cumulative trajectory errors. However, conventional LCD methods often encounter challenges when dealing with variations in illumination, changes in viewpoint, and environments with weak textures. This is due to their reliance on low-level geometric or image features. To address these issues, we propose a robust LCD method named SL-LCD, which integrates semantic information and line features to fully leverage the semantic content and line attributes within indoor scenes, thereby establishing a reliable feature correspondence between query images and loop closure images. For the retrieval of candidate closed-loop images, we construct a semantic-line-segment topological graph and introduce a graph matching algorithm to perform the LCD task. This approach fully exploits image features and spatial information to achieve closed-loop detection in complex indoor scenes. Furthermore, we present a semantic voxel-based generalized ICP (SVGICP) closed-loop relocalization algorithm tailored for challenging and complex indoor scenes, enhancing the accuracy of closed-loop relocalization in such scenarios. Experimental results demonstrate that the SL-LCD algorithm proposed in this paper surpasses state-of-the-art methods, accurately detecting closed loops, and effectively eliminating trajectory drift.

Interpreting Geometric Constructions in Artworks through Capsule Network Modeling

Abstract Interpreting the geometric structure of artworks enhances our intuitive grasp of their deeper meanings. This study employs a Capsule network model, incorporating a dynamic routing algorithm to correlate high and low-level geometric structural features of artworks. Additionally, an attention mechanism is introduced, forming a spatial attention capsule to capture the spatial context of the artwork’s geometric structure. To obtain images, a fixed-focus camera is utilized, followed by median filtering for image preprocessing and threshold segmentation using the maximum inter-class variance method to optimize recognition accuracy. The efficacy of the geometric structure recognition model, grounded in the Capsule network, is confirmed using a dataset of collected artwork images. The model achieves stability after 380 epochs, exhibiting an impressive accuracy of approximately 99.7% and a minimal loss of 0.025. Removing the attention mechanism results in a 4.06 percentage point decrease in model accuracy, whereas incorporating a dynamic routing algorithm boosts efficiency by 7.36%. Thus, the Capsule model proves highly effective in precisely recognizing and interpreting the geometric structures of artworks.

Towards Accurate Loop Closure Detection in Semantic SLAM With 3D Semantic Covisibility Graphs

Loop closure is necessary for correcting errors accumulated in simultaneous localization and mapping (SLAM) in unknown environments. However, conventional loop closure methods based on low-level geometric or image features may cause high ambiguity by not distinguishing similar scenarios. Thus, incorrect loop closures can occur. Though semantic 2D image information is considered in some literature to detect loop closures, there is little work that compares 3D scenes as an integral part of a semantic SLAM system. This letter introduces an approach, called SmSLAM+LCD, integrated into a semantic SLAM system to combine high-level 3D semantic information and low-level feature information to conduct accurate loop closure detection and effective drift reduction. The effectiveness of our approach is demonstrated in testing results.

A UAV Visual Relocalization Method Using Semantic Object Features Based on Internet of Things

Unmanned Air Vehicle (UAV) has the advantages of high autonomy and strong dynamic deployment capabilities. At the same time, with the rapid development of the Internet of Things (IoT) technology, the construction of the IoT based on UAVs can break away from the traditional single-line communication mode of UAVs and control terminals, which makes the UAVs more intelligent and flexible when performing tasks. When using UAVs to perform IoT tasks, it is necessary to track the UAVs’ position and pose at all times. When the position and pose tracking fails, relocalization is required to restore the current position and pose. Therefore, how to perform UAV relocalization accurately by using visual information has attracted much attention. However, the complex changes in light conditions in the real world have brought huge challenges to the visual relocalization of UAV. Traditional visual relocalization algorithms mostly rely on artificially designed low-level geometric features which are sensitive to light conditions. In this paper, oriented to the UAV-based IoT, a UAV visual relocalization method using semantic object features is proposed. Specifically, the method uses YOLOv3 as the object detection framework to extract the semantic information in the picture and uses the semantic information to construct a topological map as a sparse description of the environment. With prior knowledge of the map, the random walk algorithm is used on the association graphs to match the semantic features and the scenes. Finally, the EPnP algorithm is used to solve the position and pose of the UAV which will be returned to the IoT platform. Simulation results show that the method proposed in this paper can achieve robust real-time UAVs relocalization when the scene lighting conditions change dynamically and provide a guarantee for UAVs to perform IoT tasks.

LiDAR Odometry and Mapping Based on Semantic Information for Outdoor Environment

Simultaneous Localization and Mapping (SLAM) in an unknown environment is a crucial part for intelligent mobile robots to achieve high-level navigation and interaction tasks. As one of the typical LiDAR-based SLAM algorithms, the Lidar Odometry and Mapping in Real-time (LOAM) algorithm has shown impressive results. However, LOAM only uses low-level geometric features without considering semantic information. Moreover, the lack of a dynamic object removal strategy limits the algorithm to obtain higher accuracy. To this end, this paper extends the LOAM pipeline by integrating semantic information into the original framework. Specifically, we first propose a two-step dynamic objects filtering strategy. Point-wise semantic labels are then used to improve feature extraction and searching for corresponding points. We evaluate the performance of the proposed method in many challenging scenarios, including highway, country and urban from the KITTI dataset. The results demonstrate that the proposed SLAM system outperforms the state-of-the-art SLAM methods in terms of accuracy and robustness.

A Trainable Monogenic ConvNet Layer Robust in Front of Large Contrast Changes in Image Classification

Convolutional Neural Networks (ConvNets) at present achieve remarkable performance in image classification tasks. However, current ConvNets cannot guarantee the capabilities of the mammalian visual systems such as invariance to contrast and illumination changes. Some ideas to overcome the illumination and contrast variations usually have to be tuned manually and tend to fail when tested with other types of data degradation. In this context, we present a new bio-inspired entry layer, M6, which detects low-level geometric features (lines, edges, and orientations) which are similar to patterns detected by the V1 visual cortex. This new trainable layer is capable of coping with image classification even with large contrast variations. The explanation for this behavior is the monogenic signal geometry, which represents each pixel value in a 3D space using quaternions, a fact that confers a degree of explainability to the networks. We compare M6 with a conventional convolutional layer (C) and a deterministic quaternion local phase layer (Q9). The experimental setup is designed to evaluate the robustness of our M6 enriched ConvNet model and includes three architectures, four datasets, three types of contrast degradation (including non-uniform haze degradations). The numerical results reveal that the models with M6 are the most robust in front of any kind of contrast variations. This amounts to a significant enhancement of the C models, which usually have reasonably good performance only when the same training and test degradation are used, except for the case of maximum degradation. Moreover, the Structural Similarity Index Measure (SSIM) is used to analyze and explain the robustness effect of the M6 feature maps under any kind of contrast degradations.

3D INDOOR MAPPING WITH THE MICROSOFT HOLOLENS: QUALITATIVE AND QUANTITATIVE EVALUATION BY MEANS OF GEOMETRIC FEATURES

Abstract. 3D indoor mapping and scene understanding have seen tremendous progress in recent years due to the rapid development of sensor systems, reconstruction techniques and semantic segmentation approaches. However, the quality of the acquired data strongly influences the accuracy of both reconstruction and segmentation. In this paper, we direct our attention to the evaluation of the mapping capabilities of the Microsoft HoloLens in comparison to high-quality TLS systems with respect to 3D indoor mapping, feature extraction and semantic segmentation. We demonstrate how a set of rather interpretable low-level geometric features and the resulting semantic segmentation achieved with a Random Forest classifier applied on these features are affected by the quality of the acquired data. The achieved results indicate that, while allowing for a fast acquisition of room geometries, the HoloLens provides data with sufficient accuracy for a wide range of applications.

3D shape segmentation via shape fully convolutional networks

We propose a novel fully convolutional network architecture for shapes, denoted by Shape Fully Convolutional Networks (SFCN). 3D shapes are represented as graph structures in the SFCN architecture, based on novel graph convolution and pooling operations, which are similar to convolution and pooling operations used on images. Meanwhile, to build our SFCN architecture in the original image segmentation fully convolutional network (FCN) architecture, we also design and implement a generating operation with bridging function. This ensures that the convolution and pooling operation we have designed can be successfully applied in the original FCN architecture. In this paper, we also present a new shape segmentation approach based on SFCN. Furthermore, we allow more general and challenging input, such as mixed datasets of different categories of shapes which can prove the ability of our generalisation. In our approach, SFCNs are trained triangles-to-triangles by using three low-level geometric features as input. Finally, the feature voting-based multi-label graph cuts is adopted to optimise the segmentation results obtained by SFCN prediction. The experiment results show that our method can effectively learn and predict mixed shape datasets of either similar or different characteristics, and achieve excellent segmentation results.

A Hybrid Semantic Point Cloud Classification-Segmentation Framework Based on Geometric Features and Semantic Rules

In this paper, we focus on semantic point cloud classification taking into account standard failure cases reported in a variety of investigations. We present a hybrid two-step framework integrating classification, segmentation and semantic rules in a common end-to-end processing pipeline from irregularly distributed points to semantically labelled point clouds. The first step of our framework consists of a point-wise semantic point cloud classification based on rather intuitive, handcrafted, low-level geometric features extracted from local neighbourhoods of locally adaptive size. The second step of our framework consists of refining the point-wise classification results by considering semantic rules applied to geometric features extracted on the basis of an over-segmentation of the derived class-wise point clouds. We demonstrate the performance of our framework on a standard benchmark dataset for which we obtain a semantic labelling of high accuracy and high plausibility.

From low-level geometric features to high-level semantics: An axiomatic fuzzy set clustering approach

In this paper, we developed a new method to extract semantic facial descriptions by using an Axiomatic Fuzzy Set (AFS)-based clustering approach. Landmark-based geometry features are first used to represent facial components, and then we developed a new feature selection algorithm to select salient features based on feature similarities defined in AFS. Finally, the AFS-based clustering technique was used to extract the high-level semantic concepts. Extensive experiments showed that the proposed method can achieve much better results than the conventional clustering approaches like K-means and Fuzzy c-means clustering (FCM).

Localization from semantic observations via the matrix permanent

Most approaches to robot localization rely on low-level geometric features such as points, lines, and planes. In this paper, we use object recognition to obtain semantic information from the robot’s sensors and consider the task of localizing the robot within a prior map of landmarks, which are annotated with semantic labels. As object recognition algorithms miss detections and produce false alarms, correct data association between the detections and the landmarks on the map is central to the semantic localization problem. Instead of the traditional vector-based representation, we propose a sensor model, which encodes the semantic observations via random finite sets and enables a unified treatment of missed detections, false alarms, and data association. Our second contribution is to reduce the problem of computing the likelihood of a set-valued observation to the problem of computing a matrix permanent. It is this crucial transformation that allows us to solve the semantic localization problem with a polynomial-time approximation to the set-based Bayes filter. Finally, we address the active semantic localization problem, in which the observer’s trajectory is planned in order to improve the accuracy and efficiency of the localization process. The performance of our approach is demonstrated in simulation and in real environments using deformable-part-model-based object detectors. Robust global localization from semantic observations is demonstrated for a mobile robot, for the Project Tango phone, and on the KITTI visual odometry dataset. Comparisons are made with the traditional lidar-based geometric Monte Carlo localization.

What Do Thirty-One Columns Say about a “Theoretical” Thirty-Second?

Over the past three decades, the introduction of digital technologies in the field of architectural documentation has profoundly changed tools and acquisition techniques. Most of the developments concern metrical and colorimetric characteristics of the objects studied. These developments, surrounding the practice of architectural survey, tend to respond primarily to the requirements of completeness. In this context, it seems necessary to assess the impact of these instruments on the cognitive value of architectural representation. With a strong technological presence, the study of the built heritage is facing a problem of “information overload.” Indeed, this strong technological presence fails to strengthen representation in its role as a vehicle of knowledge. Confronted with the intelligibility deficit, this article proposes an original approach for reading morphological features of an artifact by using a bottom-up approach: the meaning of elements (i.e., their semantic layouts) come from a statistical analysis of the major shape discontinuities of a collection of instances. The idea is to rely on data accumulation to render apparent high-level semantic structures from the comparative analysis of common low-level geometric features. The principles introduced are illustrated by the study of 31 columns of the cloister of the abbey of Saint-Michel-de-Cuxa. To summarize, the first objective is to understand how digital technologies can help us in the analysis of artistic and technical production of Romanesque columns. The second objective is to automatically identify the common semantic articulations of the entire collection to build a reference model for the future assessment of each artifact.

An efficient 3D face recognition approach based on the fusion of novel local low-level features

We present a novel 3D face recognition approach based on low-level geometric features that are collected from the eyes-forehead and the nose regions. These regions are relatively less influenced by the deformations that are caused by facial expressions. The extracted features revealed to be efficient and robust in the presence of facial expressions. A region-based histogram descriptor computed from these features is used to uniquely represent a 3D face. A Support Vector Machine (SVM) is then trained as a classifier based on the proposed histogram descriptors to recognize any test face. In order to combine the contributions of the two facial regions (eyes-forehead and nose), both feature-level and score-level fusion schemes have been tested and compared. The proposed approach has been tested on FRGC v2.0 and BU-3DFE datasets through a number of experiments and a high recognition performance was achieved. Based on the results of “neutral vs. non-neutral” experiment of FRGC v2.0 and “low-intensity vs. high-intensity” experiment of BU-3DFE, the feature-level fusion scheme achieved verification rates of 97.6% and 98.2% at 0.1% False Acceptance Rate (FAR) and identification rates of 95.6% and 97.7% on the two datasets respectively. The experimental results also have shown that the feature-level fusion scheme outperformed the score-level fusion one.

Visual style: Qualitative and context-dependent categorization

Style is an ordering principle by which to structure artifacts in a design domain. The application of a visual order entails some explicit grouping property that is both cognitively plausible and contextually dependent. Central to cognitive–contextual notions are the type of representation used in analysis and the flexibility to allow semantic interpretation. We present a model of visual style based on the concept of similarity as a qualitative context-dependent categorization. The two core components of the model are semantic feature extraction and self-organizing maps (SOMs). The model proposes a method of categorizing two-dimensional unannotated design diagrams using both low-level geometric and high-level semantic features that are automatically derived from the pictorial content of the design. The operation of the initial model, called Q-SOM, is then extended to include relevance feedback (Q-SOM:RF). The extended model can be seen as a series of sequential processing stages, in which qualitative encoding and feature extraction are followed by iterative recategorization. Categorization is achieved using an unsupervised SOM, and contextual dependencies are integrated via cluster relevance determined by the observer's feedback. The following stages are presented: initial per feature detection and extraction, selection of feature sets corresponding to different spatial ontologies, unsupervised categorization of design diagrams based on appropriate feature subsets, and integration of design context via relevance feedback. From our experiments we compare different outcomes from consecutive stages of the model. The results show that the model provides a cognitively plausible and context-dependent method for characterizing visual style in design.