Semi-supervised 3D place categorisation by descriptor clustering

Magnusson, Martin; Kucner, Tomasz Piotr; Shahbandi, Saeed Gholami; Andreasson, Henrik; Lilienthal, Achim J.

doi:10.1109/iros.2017.8202216

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…Moreover, NDT can be used to provide place signatures for loop closure detection, e.g. using NDT histograms [24] and semantic-NDT histograms [25], semi-supervised place categorisation [26], and to create interest point descriptors [27] for efficient registration or localisation.…”

Section: B Ndt-based Localisationmentioning

confidence: 99%

NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation

Zhou

Zhao

Adolfsson

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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3D point cloud-based place recognition is highly demanded by autonomous driving in GPS-challenged environments and serves as an essential component (i.e. loopclosure detection) in lidar-based SLAM systems. This paper proposes a novel approach, named NDT-Transformer, for realtime and large-scale place recognition using 3D point clouds. Specifically, a 3D Normal Distribution Transform (NDT) representation is employed to condense the raw, dense 3D point cloud as probabilistic distributions (NDT cells) to provide the geometrical shape description. Then a novel NDT-Transformer network learns a global descriptor from a set of 3D NDT cell representations. Benefiting from the NDT representation and NDT-Transformer network, the learned global descriptors are enriched with both geometrical and contextual information. Finally, descriptor retrieval is achieved using a query-database for place recognition. Compared to the state-of-the-art methods, the proposed approach achieves an improvement of 7.52% on average top 1 recall and 2.73% on average top 1% recall on the Oxford Robotcar benchmark. †Authors contribute equally.This project is funded by EPSRC FAIR-SPACE Hub (EP/R026092/1), Royal Society RGS/R2/202432 and EU Horizon 2020 ILIAD (No. 732737).

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Section: B Ndt-based Localisationmentioning

confidence: 99%

NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation

Zhou

Zhao

Adolfsson

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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“…It encodes information about the shapes and orientations of the normal distributions over varying ranges from the sensor. It has been used both for loop closure and place categorization [13].…”

Section: Related Workmentioning

confidence: 99%

Semantically Assisted Loop Closure in SLAM Using NDT Histograms

Zaganidis

Zerntev

Duckett

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Precise knowledge of pose is of great importance for reliable operation of mobile robots in outdoor environments. Simultaneous localization and mapping (SLAM) is the online construction of a map during exploration of an environment. One of the components of SLAM is loop closure detection, identifying that the same location has been visited and is present on the existing map, and localizing against it. We have shown in previous work that using semantics from a deep segmentation network in conjunction with the Normal Distributions Transform (NDT) point cloud registration improves the robustness, speed and accuracy of lidar odometry. In this work we extend the method for loop closure detection, using the labels already available from local registration into NDT Histograms, and we present a SLAM pipeline based on Semantic assisted NDT and PointNet++. We experimentally demonstrate on sequences from the KITTI benchmark that the map descriptor we propose outperforms NDT Histograms without semantics, and we validate its use on a SLAM task.

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“…Occupancy maps represent the shape of the environment through a grid, where each cell is associated to a probability of being occupied by an obstacle [1]. However, such maps do not provide higher-level semantic information about the environment (i.e., the type of objects, the structure of the environment, the room type [2], [3]).…”

Section: Introductionmentioning

confidence: 99%

Robust Structure Identification and Room Segmentation of Cluttered Indoor Environments from Occupancy Grid Maps

Luperto¹,

Kucner²,

Tassi³

et al. 2022

Preprint

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Identifying the environment's structure, i.e., to detect core components as rooms and walls, can facilitate several tasks fundamental for the successful operation of indoor autonomous mobile robots, including semantic environment understanding. These robots often rely on 2D occupancy maps for core tasks such as localisation, motion and task planning. However, reliable identification of structure and room segmentation from 2D occupancy maps is still an open problem due to clutter (e.g., furniture and movable object), occlusions, and partial coverage. We propose a method for the RObust StructurE identification and ROom SEgmentation (ROSE 2 ) of 2D occupancy maps, which may be cluttered and incomplete. ROSE 2 identifies the main directions of walls and is resilient to clutter and partial observations, allowing to extract a clean, abstract geometrical floor-plan-like description of the environment, which is used to segment, i.e., to identify rooms in, the original occupancy grid map. ROSE 2 is tested in several realworld publicly-available cluttered maps obtained in different conditions. The results show how it can robustly identify the environment structure in 2D occupancy maps suffering from clutter and partial observations, while significantly improving room segmentation accuracy. Thanks to the combination of clutter removal and robust room segmentation ROSE 2 consistently achieves higher performance than the state-of-the-art methods, against which it is compared.

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Semi-supervised 3D place categorisation by descriptor clustering

Cited by 6 publications

References 15 publications

NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation

NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation

Semantically Assisted Loop Closure in SLAM Using NDT Histograms

Robust Structure Identification and Room Segmentation of Cluttered Indoor Environments from Occupancy Grid Maps

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