Geometry matters: Place recognition in 2D range scans using Geometrical Surface Relations

Himstedt, Marian; Maehle, Erik

doi:10.1109/ecmr.2015.7324185

Cited by 12 publications

(5 citation statements)

References 17 publications

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“…Despite this condition, the results show that even with a considerable map compression rate (88% for FR079 and 85% Intel data sets, respectively) our approach maintains high loop closure and global localization performance. This conclusion is also valid when comparing to the work of Himstedt et al [23,24] on GLARE features and GSR signature. The authors obtain good recall and precision levels but require complex signature calculations and do not consider map data reduction the way it is done here.…”

Section: Discussion With Regard To State-of-the-artsupporting

confidence: 85%

Stable keypoints selection for 2D LiDAR based place recognition with map data reduction

2022

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This paper presents a new feature based approach for place recognition using 2D LiDAR (Light Detection And Ranging) data. The main contribution lies in the mapping process. It includes a keypoint selection strategy to model places with persistent keypoints from concatenated LiDAR scans. Our objective is to achieve map data reduction while maintaining good place recognition performace. LiDAR scans are concatenated with a registration algorithm and keypoints are extracted from each scan. Based on a regular grid, our approach measures the occurrence of similar keypoints in each region of interest defined by a grid cell. Only keypoints with occurrences beyond a threshold, qualified as stable keypoints, are kept in the place model called submap. The environment is therefore modeled as a collection of submaps, which constitutes the global map. Place recognition consists in submap matching followed by a two steps geometric verification. In the first stage, optimal parameters are set using corrected data. Mapping parameters satisfy six criteria among which is the spatial distribution distance, which represents another contribution of our work. It gives a measure on how well keypoints are distributed in space. Place recognition optimal parameters are set through global localization. In the second stage, the approach is evaluated using raw data in the contexts of global localization, and loop closure detection in a SLAM framework. The results obtained using popular real data sets show that our approach achieves significant map data reduction (up to $92\%$ ) while maintaining good place recognition performance, comparable to state-of-the-art methods.

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Section: Discussion With Regard To State-of-the-artsupporting

confidence: 85%

Stable keypoints selection for 2D LiDAR based place recognition with map data reduction

2022

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“…However, the full connection layer loses the spatial information of the image, which might not be ideal for applications such as visual position recognition. The experimental results of Himstedt et al 34 and Bai et al 35 show that in loop closure detection, the depth feature generated by the convolution layer has better performance than that of the fully connected layer. Based on these findings, we chose conv4-3 of the DCNN to extract the image features.…”

Section: Methodsmentioning

confidence: 99%

“…The research first resumed the dense 3D semantic map of the environment and its topology map; secondly, the association information between the semantic map and the objects in the topology map was used to achieve location identification. The geometric landmark relationship between the objects (glare) 33,34 has been proposed to convert the scan points into a histogram representation with constant pose and to evaluate the constant distances and angles between the points. However, this approach is sensitive to the resolution of discretized geometric parameters.…”

Section: Related Workmentioning

confidence: 99%

Location recognition of unmanned vehicles based on visual semantic information and geometric distribution

Cao

Huang

Shu

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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Due to the influence of weather, light, season and angle changes, the appearance of objects changes visually, which makes it difficult for unmanned vehicles that rely on visual positioning to complete their positioning work. This paper proposes a coordinated positioning method that is composed of semantic information and geometric relationships distribution (GRD), which improves the robustness of unmanned vehicle location under the above conditions. First, we improved the FAST-SCNN semantic segmentation network and replaced its fully connected layer with the conv4-3 module to prevent the spatial information of the image from being lost in the fully connected layer. At the same time, the conv4-3 layer contains the richest semantic information, we use image semantic content to create a dense and prominent scene description. These prominent descriptions were learned from a large data set of perceptual changes. The method can accurately segment geometrically stable image regions. We combine the characteristics of these highlighted areas with the existing overall representation to produce a more robust scene descriptor. Second, a method is designed to integrate the matching of semantic labels and geometric distribution relations, which is a new closed loop location recognition label and landmark map. The geometric pair relationship between the ground marks is encoded as a continuous probability density function, the GRD function, which is expressed by a Laguerre polynomial and Fourier series basis expansion. This orthogonal basis representation allows for efficient computation of rotation and translation invariants, which are used to compare signatures and search for potential loop closure candidates. Finally, we evaluate our method with some of the most advanced algorithms, such as OpenSeqSLAM, AlexNet and VSO, to demonstrate its advantages. The experimental results for representative data sets show that the method based on Fast-SCNN is superior to other methods.

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“…Often they critically rely on additional pieces of information available in their target scenario -to prune the association hypothesis space, or obtain strong indirect priors on scene similarity, or enable construction of aggregated spatial information structures to allow its estimation (for instance, [25,11,7,32,6,3]) 2 . Approaches also often operate under limited changes in viewpoint and / or on specific types of scene geometry (such as [42,11,29]) or they solve a simplified 2D problem (such as [19]). Understandably, methodologies like above are either use case limited or restrictive.…”

Section: Introductionmentioning

confidence: 99%

Purely Geometric Scene Association and Retrieval - A Case for Macro Scale 3D Geometry

Sawhney,

Li,

Christensen

et al. 2018

Preprint

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We address the problems of measuring geometric similarity between 3D scenes, represented through point clouds or range data frames, and associating them. Our approach leverages macro-scale 3D structural geometry -the relative configuration of arbitrary surfaces and relationships among structures that are potentially far apart. We express such discriminative information in a viewpoint-invariant feature space. These are subsequently encoded in a frame-level signature that can be utilized to measure geometric similarity. Such a characterization is robust to noise, incomplete and partially overlapping data besides viewpoint changes. We show how it can be employed to select a diverse set of data frames which have structurally similar content, and how to validate whether views with similar geometric content are from the same scene. The problem is formulated as one of general purpose retrieval from an unannotated, spatio-temporally unordered database. Empirical analysis indicates that the presented approach thoroughly outperforms baselines on depth / range data. Its depth-only performance is competitive with state-of-the-art approaches with RGB or RGB-D inputs, including ones based on deep learning. Experiments show retrieval performance to hold up well with much sparser databases, which is indicative of the approach's robustness. The approach generalized wellit did not require dataset specific training, and scaled up in our experiments. Finally, we also demonstrate how geometrically diverse selection of views can result in richer 3D reconstructions.

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Geometry matters: Place recognition in 2D range scans using Geometrical Surface Relations

Cited by 12 publications

References 17 publications

Stable keypoints selection for 2D LiDAR based place recognition with map data reduction

Stable keypoints selection for 2D LiDAR based place recognition with map data reduction

Location recognition of unmanned vehicles based on visual semantic information and geometric distribution

Purely Geometric Scene Association and Retrieval - A Case for Macro Scale 3D Geometry

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