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- Flood, Gabrielle, et al.
(författare)
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Efficient Merging of Maps and Detection of Changes
- 2019
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Ingår i: Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings. - Cham : Springer International Publishing. - 0302-9743 .- 1611-3349. - 9783030202040 ; 11482 LNCS, s. 348-360
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Konferensbidrag (refereegranskat)abstract
- With the advent of cheap sensors and computing capabilities as well as better algorithms it is now possible to do structure from motion using crowd sourced data. Individual estimates of a map can be obtained using structure from motion (SfM) or simultaneous localization and mapping (SLAM) using e.g. images, sound or radio. However the problem of map merging as used for collaborative SLAM needs further attention. In this paper we study the basic principles behind map merging and collaborative SLAM. We develop a method for merging maps – based on a small memory footprint representation of individual maps – in a way that is computationally efficient. We also demonstrate how the same framework can be used to detect changes in the map. This makes it possible to remove inconsistent parts before merging the maps. The methods are tested on both simulated and real data, using both sensor data from radio sensors and from cameras.
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| 2. |
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| 3. |
- Flood, Gabrielle, et al.
(författare)
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Minimal Solvers for Point Cloud Matching with Statistical Deformations
- 2022
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Ingår i: 2022 26th International Conference on Pattern Recognition (ICPR). - 9781665490634
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Konferensbidrag (refereegranskat)abstract
- An important issue in simultaneous localisation and mapping is how to match and merge individual local maps into one global map. This is addressed within the field of robotics and is crucial for multi-robot SLAM. There are a number of different ways to solve this task depending on the representation of the map. To take advantage of matching and merging methods that allow for deformations of the local maps it is important to find feature matches that capture such deformations. In this paper we present minimal solvers for point cloud matching using statistical deformations. The solvers use either three or four point matches. These solve for either rigid or similarity transformation as well as shape deformation in the direction of the most important modes of variation. Given an initial set of tentative matches based on, for example, feature descriptors or machine learning we use these solvers in a RANSAC loop to remove outliers among the tentative matches. We evaluate the methods on both synthetic and real data and compare them to RANSAC methods based on Procrustes and demonstrate that the proposed methods improve on the current state-of-the-art.
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| 4. |
- Gillsjö, David
(författare)
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Applications in Monocular Computer Vision using Geometry and Learning : Map Merging, 3D Reconstruction and Detection of Geometric Primitives
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- As the dream of autonomous vehicles moving around in our world comes closer, the problem of robust localization and mapping is essential to solve. In this inherently structured and geometric problem we also want the agents to learn from experience in a data driven fashion. How the modern Neural Network models can be combined with Structure from Motion (SfM) is an interesting research question and this thesis studies some related problems in 3D reconstruction, feature detection, SfM and map merging.In Paper I we study how a Bayesian Neural Network (BNN) performs in Semantic Scene Completion, where the task is to predict a semantic 3D voxel grid for the Field of View of a single RGBD image. We propose an extended task and evaluate the benefits of the BNN when encountering new classes at inference time. It is shown that the BNN outperforms the deterministic baseline.Papers II-III are about detection of points, lines and planes defining a Room Layout in an RGB image. Due to the repeated textures and homogeneous colours of indoor surfaces it is not ideal to only use point features for Structure from Motion. The idea is to complement the point features by detecting a Wireframe – a connected set of line segments – which marks the intersection of planes in the Room Layout. Paper II concerns a task for detecting a Semantic Room Wireframe and implements a Neural Network model utilizing a Graph Convolutional Network module. The experiments show that the method is more flexible than previous Room Layout Estimation methods and perform better than previous Wireframe Parsing methods. Paper III takes the task closer to Room Layout Estimation by detecting a connected set of semantic polygons in an RGB image. The end-to-end trainable model is a combination of a Wireframe Parsing model and a Heterogeneous Graph Neural Network. We show promising results by outperforming state of the art models for Room Layout Estimation using synthetic Wireframe detections. However, the joint Wireframe and Polygon detector requires further research to compete with the state of the art models.In Paper IV we propose minimal solvers for SfM with parallel cylinders. The problem may be reduced to estimating circles in 2D and the paper contributes with theory for the twoview relative motion and two-circle relative structure problem. Fast solvers are derived and experiments show good performance in both simulation and on real data.Papers V-VII cover the task of map merging. That is, given a set of individually optimized point clouds with camera poses from a SfM pipeline, how can the solutions be effectively merged without completely resolving the Structure from Motion problem? Papers V-VI introduce an effective method for merging and shows the effectiveness through experiments of real and simulated data. Paper VII considers the matching problem for point clouds and proposes minimal solvers that allows for deformation ofeach point cloud. Experiments show that the method robustly matches point clouds with drift in the SfM solution.
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| 5. |
- Gillsjö, David, et al.
(författare)
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In Depth Bayesian Semantic Scene Completion
- 2021
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Ingår i: 2020 25th International Conference on Pattern Recognition (ICPR). - 1051-4651. - 9781728188089 ; , s. 6335-6342
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Konferensbidrag (refereegranskat)
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| 6. |
- Gillsjö, David, et al.
(författare)
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Polygon Detection for Room Layout Estimation using Heterogeneous Graphs and Wireframes
- 2023
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Ingår i: Proceedings - 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023. - 9798350307443 ; , s. 1-10
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Konferensbidrag (refereegranskat)abstract
- This paper presents a neural network based semantic plane detection method utilizing polygon representations. The method can for example be used to solve room layout estimations tasks and is built on, combines and further develops several different modules from previous research. The network takes an RGB image and estimates a wireframe as well as a feature space using an hourglass backbone. From these, line and junction features are sampled. The lines and junctions are then represented as an undirected graph, from which polygon representations of the sought planes are obtained. Two different methods for this last step are investigated, where the most promising method is built on a heterogeneous graph transformer. The final output is in all cases a projection of the semantic planes in 2D. The methods are evaluated on the Structured3D dataset and we investigate the performance both using sampled and estimated wireframes. The experiments show the potential of the graph-based method by outperforming state of the art methods in Room Layout estimation in the 2D metrics using synthetic wireframe detections.
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| 7. |
- Gillsjö, David, et al.
(författare)
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Semantic Room Wireframe Detection from a Single View
- 2022
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Ingår i: 26th International Conference on Pattern Recognition, 2022. - 9781665490627 - 9781665490634 ; , s. 1886-1893
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Konferensbidrag (refereegranskat)abstract
- Reconstruction of indoor surfaces with limited texture information or with repeated textures, a situation common in walls and ceilings, may be difficult with a monocular Structure from Motion system. We propose a Semantic Room Wireframe Detection task to predict a Semantic Wireframe from a single perspective image. Such predictions may be used with shape priors to estimate the Room Layout and aid reconstruction. To train and test the proposed algorithm we create a new set of annotations from the simulated Structured3D dataset. We show qualitatively that the SRW-Net handles complex room geometries better than previous Room Layout Estimation algorithms while quantitatively out-performing the baseline in non-semantic Wireframe Detection.
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| 8. |
- Tegler, Erik, et al.
(författare)
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The Multi-view Geometry of Parallel Cylinders
- 2023
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Ingår i: Image Analysis : 23rd Scandinavian Conference, SCIA 2023, Sirkka, Finland, April 18–21, 2023, Proceedings - 23rd Scandinavian Conference, SCIA 2023, Sirkka, Finland, April 18–21, 2023, Proceedings. - 1611-3349 .- 0302-9743. - 9783031314377 - 9783031314384 ; 13886, s. 482-499
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Konferensbidrag (refereegranskat)abstract
- In this paper we study structure from motion problems for parallel cylinders. Using sparse keypoint correspondences is an efficient (and standard) way to solve the structure from motion problem. However, point features are sometimes unavailable and they can be unstable over time and viewing conditions. Instead, we propose a framework based on silhouettes of quadric surfaces, with special emphasis on parallel cylinders. Such structures are quite common, e.g. trees, lampposts, pillars, and furniture legs. Traditionally, the projection of the center lines of such cylinders have been considered and used in computer vision. Here, we demonstrate that the apparent width of the cylinders also contains useful information for structure and motion estimation. We provide mathematical analysis of relative structure and relative motion tensors, which is used to develop a number of minimal solvers for simultaneously estimating camera pose and scene structure from silhouette lines of cylinders. These solvers can be used efficiently in robust estimation schemes, such as RANSAC. We use Sampson-approximation methods for efficient estimation using over-determined data and develop averaging techniques. We also perform synthetic accuracy and robustness tests and evaluate our methods on a number of real-world scenarios.
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