Structure SLAM with points, planes and objects

Zhou, Benchun; Gilles, Maximilian; Meng, Yongqi

doi:10.1080/01691864.2022.2123253

Cited by 3 publications

(6 citation statements)

References 32 publications

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“…Contrasting with the plane extraction technique in [ 27 , 28 ], the method proposed herein capitalizes on known map information to extract plane point clouds. The method introduced in [ 30 ] should meet the criteria that planes be parallel or perpendicular to one another, a strict criterion that restricts localization application. Within numerous well-designed structures, corridors are not always aligned horizontally and vertically.…”

Section: Discussionmentioning

confidence: 99%

“…[ 29 ] utilized the absolute ground plane to constrain vertical pose estimation, subsequently reducing the estimation error. A point-plane-object localization system was proposed for semantic map reconstruction [ 30 ], which demonstrated effective localization in indoor scenarios. However, the method in [ 30 ] necessitates strict criteria, such as planes being parallel or perpendicular to one another.…”

Section: Introductionmentioning

confidence: 99%

“…A point-plane-object localization system was proposed for semantic map reconstruction [ 30 ], which demonstrated effective localization in indoor scenarios. However, the method in [ 30 ] necessitates strict criteria, such as planes being parallel or perpendicular to one another. Additionally, in both [ 27 , 28 ], planes are identified solely by analyzing point clouds.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Localization on a-priori information of plane extraction

Zhao

Misyurin

et al. 2023

PLoS ONE

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Localization constitutes a critical challenge for autonomous mobile robots, with flattened walls serving as a fundamental reference for indoor localization. In numerous scenarios, prior knowledge of a wall’s surface plane is available, such as planes in building information modeling (BIM) systems. This article presents a localization technique based on a-priori plane point cloud extraction. The position and pose of the mobile robot are estimated through real-time multi-plane constraints. An extended image coordinate system is proposed to represent any planes in space and establish correspondences between visible planes and those in the world coordinate system. Potentially visible points representing the constrained plane in the real-time point cloud are filtered using the filter region of interest (ROI), derived from the theoretical visible plane region within the extended image coordinate system. The number of points representing the plane influences the calculation weight in the multi-plane localization approach. Experimental validation of the proposed localization method demonstrates its allowance for redundancy in initial position and pose error.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Localization on a-priori information of plane extraction

Zhao

Misyurin

et al. 2023

PLoS ONE

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“…An occupancy map (Grisetti et al, 2007) is well-studied and widely used for planar navigation in an indoor environment, it decomposes the space into a fixed-size grid and each cell of the grid indicates whether this area is occupied or not. A feature-based map (Zhou et al, 2022) represents the environment as different landmarks, including feature points, geometry planes, semantic objects, etc. This map provides an easy way to update, thus being useful for scene understanding and localization.…”

Section: Object-level Semantic Mappingmentioning

confidence: 99%

Efficient Object-Level Semantic Mapping with RGB-D Cameras

Zhou

Furmans

et al. 2023

Preprint

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To autonomously navigate in real-world environments, mobile robots require a dense map to guarantee safety, such as a 3D occupancy map. However, this map lacks semantic information for scene understanding. On the other hand, semantic objects can be introduced to the map with the help of deep neural networks, but they may suffer from critical run-time issues due to heavy processing components. In this paper, we present an efficient semantic mapping system to incrementally build a voxel-based map with individual objects. Firstly, a frame-wise object segmentation scheme is adopted to segment 3D objects from RGB-D images. Then, a new object association strategy with geometry and semantic descriptor is proposed to track and update object information, Finally, these objects are integrated into a CPU-based voxel mapping approach to incrementally build a global object-level volumetric map. Experiments on publicly available indoor datasets show that the proposed system achieves a good semantic mapping performance. Besides, our method outperforms other object-level mapping algorithms in terms of segmentation results and computational efficiency. Furthermore, the system is evaluated within a logistical robotic platform to demonstrate the use case in real-world applications.

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“…Convolutional Neural Networks (CNNs) have always been a concern in artificial intelligence. Since the AlexNet [1], ResNet [2] convolutional networks have been widely proven to be effective in object detection [3–9], semantic segmentation [10–15], and image classification etc. Recently, attention mechanisms in deep networks have received extensive attention, which stems from the studies on human vision and provides an efficient way for us to work.…”

Section: Introductionmentioning

confidence: 99%

An efficient mixed attention module

Sheng

Chen

2023

IET Computer Vision

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Recently, the application of attention mechanisms in convolutional neural networks (CNNs) has become a hot area in computer vision. Most existing methods focus on channel attention or spatial attention. Some mixed attention usually achieves better performance than channel attention or spatial attention with the help of a complex model structure, which increases the complexity of the model. This article proposes an efficient mixed attention that combines channel information with spatial information using learnable broadcast addition to reduce this complexity. In particular, this module can simplify learning and improve performance with fewer parameters. Furthermore, our method uses an excitation method based on the Tanh function to reduce computational resources while maintaining model performance, and it is a lightweight attention module that can be used in arbitrary CNNs to improve performance. Experiments on ImageNet and Cifar confirm the effectiveness of the proposed method. Besides, our method remains highly competitive for object detection tasks and image segmentation tasks.

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Structure SLAM with points, planes and objects

Cited by 3 publications

References 32 publications

Localization on a-priori information of plane extraction

Localization on a-priori information of plane extraction

Efficient Object-Level Semantic Mapping with RGB-D Cameras

An efficient mixed attention module

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