Multi-LiDAR Mapping for Scene Segmentation in Indoor Environments for Mobile Robots

Gonzalez, Pavel; Mora, Alicia; Garrido, Santiago; Barber, Ramón; Moreno, Lourdes

doi:10.3390/s22103690

Cited by 10 publications

(7 citation statements)

References 28 publications

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“…Although this information is essential, it is out of the scope of this paper. We depart from the previous work presented in [8], in which Harmony Search performs the optimization of robot poses to define a new approach for SLAM. The result is a set of optimized poses that will be used to iteratively update the map.…”

Section: A Coordinate Transformationsmentioning

confidence: 99%

“…This is a continuation of previous work performed in our research group. 3D information has been previously used to generate representations at various heights [8] and glass has been detected by 2D sensors as a function of reflectivity [9]. With this new proposal, both objectives are merged and a robust occupancy grid map for robot navigation is achieved.…”

Section: Introductionmentioning

confidence: 99%

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Leveraging 3-D Data for Whole Object Shape and Reflection Aware 2-D Map Building

Mora,

Barber,

Moreno

2024

IEEE Sensors J.

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Two-dimensional laser scan sensors stand out as the preferred choice for robot mapping applications. However, these sensors have a significant drawback. Encountering objects with varying shapes at different heights, such as tables, poses challenges for these sensors due to their limited detection capability resulting from their dimensionality. This limitation increases the risk of potential collisions. Additionally, there are multiple polished materials that generate noise due to reflection. In order to have a robust occupancy grid map representation, these problems must be addressed. This paper proposes the usage of a 3D laser scan sensor to generate a 2D occupancy grid map that incorporates the complete geometry of objects and effectively filters out noise from reflective materials like glass. The main novelty of the method is that it takes advantage of all the available 3D data to avoid any information loss about objects' shapes. Additionally, a new approach for filtering reflection noise based on the analysis of indoor structural elements is proposed. Both approaches are merged for the creation of a robust indoor representation that allows to safely navigate the environment. Finally, a recursive Bayesian filter is applied for merging data, so noise due to dynamic elements that appeared during data collection is also filtered. Experimental evaluations in indoor environments with diverse objects and reflective surfaces, including dynamic elements like people, demonstrate the effectiveness of the proposed approach.

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Section: A Coordinate Transformationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Leveraging 3-D Data for Whole Object Shape and Reflection Aware 2-D Map Building

Mora,

Barber,

Moreno

2024

IEEE Sensors J.

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“…Mobile robots utilize LiDAR technology for external environment perception [31]. LiDAR enables pose estimation and the construction of 2D maps for mobile robots [32] through the processing of scanned data [33]. In this technology, the forward direction of the 2D LiDAR is defined as the polar axis, while the rotation center of the LiDAR ranging core is set as the pole, establishing a polar coordinate system.…”

Section: Laser Radar Data Preprocessingmentioning

confidence: 99%

Cognitive Enhancement of Robot Path Planning and Environmental Perception Based on Gmapping Algorithm Optimization

Liu,

Gong,

et al. 2024

Electronics

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In the logistics warehouse environment, the autonomous navigation and environment perception of the logistics sorting robot are two key challenges. To deal with the complex obstacles and cargo layout in a warehouse, this study focuses on improving the robot perception and navigation system to achieve efficient path planning and safe motion control. For this purpose, a scheme based on an improved Gmapping algorithm is proposed to construct a high-precision map inside a warehouse through the efficient scanning and processing of environmental data by robots. While the improved algorithm effectively integrates sensor data with robot position information to realize the real-time modeling and analysis of warehouse environments. Consequently, the precise mapping results provide a reliable navigation basis for the robot, enabling it to make intelligent path planning and obstacle avoidance decisions in unknown or dynamic environments. The experimental results show that the robot using the improved Gmapping algorithm has high accuracy and robustness in identifying obstacles and an effectively reduced navigation error, thus improving the intelligence level and efficiency of logistics operations. The improved algorithm significantly enhances obstacle detection rates, increasing them by 4.05%. Simultaneously, it successfully reduces map size accuracy errors by 1.4% and angle accuracy errors by 0.5%. Additionally, the accuracy of the robot’s travel distance improves by 2.4%, and the mapping time is reduced by nine seconds. Significant progress has been made in achieving high-precision environmental perception and intelligent navigation, providing reliable technical support and solutions for autonomous operations in logistics warehouses.

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“…The main objective is finding a 3D representation of the environment from which 2D occupancy grid maps can be created by taking 3D information slices. The selected algorithm for creating the 3D point cloud representation is SLAM based on Harmony Search, as described in [16].…”

Section: Mapping Based On 3d Informationmentioning

confidence: 99%

Sensor Fusion for Social Navigation on a Mobile Robot Based on Fast Marching Square and Gaussian Mixture Model

Mora

Prados

Méndez

et al. 2022

Sensors

Self Cite

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Mobile robot navigation has been studied for a long time, and it is nowadays widely used in multiple applications. However, it is traditionally focused on two-dimensional geometric characteristics of the environments. There are situations in which robots need to share space with people, so additional aspects, such as social distancing, need to be considered. In this work, an approach for social navigation is presented. A multi-layer model of the environment containing geometric and topological characteristics is built based on the fusion of multiple sensor information. This is later used for navigating the environment considering social distancing from individuals and groups of people. The main novelty is combining fast marching square for path planning and navigation with Gaussian models to represent people. This combination allows to create a continuous representation of the environment from which smooth paths can be extracted and modified according to dynamically captured data. Results prove the practical application of the method on an assistive robot for navigating indoor scenarios, including a behavior for crossing narrow passages. People are efficiently detected and modeled to assure their comfort when robots are around.

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Multi-LiDAR Mapping for Scene Segmentation in Indoor Environments for Mobile Robots

Cited by 10 publications

References 28 publications

Leveraging 3-D Data for Whole Object Shape and Reflection Aware 2-D Map Building

Leveraging 3-D Data for Whole Object Shape and Reflection Aware 2-D Map Building

Cognitive Enhancement of Robot Path Planning and Environmental Perception Based on Gmapping Algorithm Optimization

Sensor Fusion for Social Navigation on a Mobile Robot Based on Fast Marching Square and Gaussian Mixture Model

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