Efficient Traversability Mapping for Service Robots Using a Point-cloud Fast Filter

Sánchez, Carlos; Ceriotti, Matteo; Capitán, Jesús; Marrón, Pedro José

doi:10.1109/icar46387.2019.8981625

Cited by 3 publications

(5 citation statements)

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“…The parameters used in the experimentation are presented in Table 2. For these experiments, we used a traversable map of the environment that accounts for the height of the robot, as built by existing libraries [22].…”

Section: Resultsmentioning

confidence: 99%

“…Of course, 3D sensor information can also be exploited to build 2D traversable maps, which are easier to compute and store, still considering important aspects like the height of the robot. For example, MONO SLAM [21] uses RGB-D cameras for this type of 2.5D maps, and our prior work, PFF [22], exploits low-end point-clouds obtained by RGB-D cameras and 3D LiDARs for the same purpose. In general, a large variety of approaches based on SLAM have been proposed to build geometric maps in 2D and 3D environments.…”

Section: Object Detection and Semantic Mappingmentioning

confidence: 99%

“…In the remaining discussion, we assume that the information about the robot position is available together with a traversable map of the environment, like the one we computed in our previous work [22]. As mentioned before, the segmentation is performed on low-density point-clouds provided, e.g., by a 3D LiDAR.…”

Section: Semantic Mapping Via Segmentationmentioning

confidence: 99%

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Semantic Mapping with Low-Density Point-Clouds for Service Robots in Indoor Environments

et al. 2020

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The advancements in the robotic field have made it possible for service robots to increasingly become part of everyday indoor scenarios. Their ability to operate and reach defined goals depends on the perception and understanding of their surrounding environment. Detecting and positioning objects as well as people in an accurate semantic map are, therefore, essential tasks that a robot needs to carry out. In this work, we walk an alternative path to build semantic maps of indoor scenarios. Instead of relying on high-density sensory input, like the one provided by an RGB-D camera, and resource-intensive processing algorithms, like the ones based on deep learning, we investigate the use of low-density point-clouds provided by 3D LiDARs together with a set of practical segmentation methods for the detection of objects. By focusing on the physical structure of the objects of interest, it is possible to remove complex training phases and exploit sensors with lower resolution but wider Field of View (FoV). Our evaluation shows that our approach can achieve comparable (if not better) performance in object labeling and positioning with a significant decrease in processing time than established approaches based on deep learning methods. As a side-effect of using low-density point-clouds, we also better support people privacy as the lower resolution inherently prevents the use of techniques like face recognition.

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Section: Resultsmentioning

confidence: 99%

Section: Object Detection and Semantic Mappingmentioning

confidence: 99%

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Semantic Mapping with Low-Density Point-Clouds for Service Robots in Indoor Environments

et al. 2020

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“…Alternatively, 3D sensors can be used with 2D SLAM methods for the creation of 2D maps. For example, the package [ 69 ], which selects the points at a certain angle to convert the 3D information into 2D; the work presented in [ 70 ] also selects the relevant points of the 3D point-cloud and takes into account the height of the robot to build traversable maps, also called 2.5 maps that improve robot 2D navigation. Figure 6 shows a comparison between a pure 2D SLAM approach and a traversable map.…”

Section: Mappingmentioning

confidence: 99%

“…The results were obtained from a pre-recorded dataset [ 63 ] and default parameter values. The sensor is located at 60 cm of height, the resolution is of 0.4° with an horizontal FoV of 360°, the sensor frequency is 10 Hz and the robot height is 1 m: ( a ) resulting map given by Gmapping with a 2D LiDAR; ( b ) traversable (2.5D) map given by Gmapping using a 3D LiDAR and the Point-Cloud Fast Filter [ 70 ].…”

Section: Figurementioning

confidence: 99%

From Perception to Navigation in Environments with Persons: An Indoor Evaluation of the State of the Art

Sánchez

Ceriotti

Capitán

et al. 2022

Sensors

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Research in the field of social robotics is allowing service robots to operate in environments with people. In the aim of realizing the vision of humans and robots coexisting in the same environment, several solutions have been proposed to (1) perceive persons and objects in the immediate environment; (2) predict the movements of humans; as well as (3) plan the navigation in agreement with socially accepted rules. In this work, we discuss the different aspects related to social navigation in the context of our experience in an indoor environment. We describe state-of-the-art approaches and experiment with existing methods to analyze their performance in practice. From this study, we gather first-hand insights into the limitations of current solutions and identify possible research directions to address the open challenges. In particular, this paper focuses on topics related to perception at the hardware and application levels, including 2D and 3D sensors, geometric and mainly semantic mapping, the prediction of people trajectories (physics-, pattern- and planning-based), and social navigation (reactive and predictive) in indoor environments.

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Point-Cloud Fast Filter for People Detection with Indoor Service Robots

Sánchez

Capitán

Ceriotti

et al. 2020

2020 Fourth IEEE International Conference on Robotic Computing (IRC)

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Efficient Traversability Mapping for Service Robots Using a Point-cloud Fast Filter

Cited by 3 publications

References 14 publications

Semantic Mapping with Low-Density Point-Clouds for Service Robots in Indoor Environments

Semantic Mapping with Low-Density Point-Clouds for Service Robots in Indoor Environments

From Perception to Navigation in Environments with Persons: An Indoor Evaluation of the State of the Art

Point-Cloud Fast Filter for People Detection with Indoor Service Robots

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