| 1. |
- Jung, Hojung, et al.
(författare)
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Indoor Place Categorization Using Co-occurrences of LBPs in Gray and Depth Images from RGB-D Sensors
- 2014
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Ingår i: 2014 Fifth International Conference on Emerging Security Technologies. - : IEEE. - 9781479970070 ; , s. 40-45
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Konferensbidrag (refereegranskat)abstract
- Indoor place categorization is an important capability for service robots working and interacting in human environments. This paper presents a new place categorization method which uses information about the spatial correlation between the different image modalities provided by RGB-D sensors. Our approach applies co-occurrence histograms of local binary patterns (LBPs) from gray and depth images that correspond to the same indoor scene. The resulting histograms are used as feature vectors in a supervised classifier. Our experimental results show the effectiveness of our method to categorize indoor places using RGB-D cameras.
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| 2. |
- Jung, Hojung, et al.
(författare)
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Local N-ary Patterns : a local multi-modal descriptor for place categorization
- 2016
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Ingår i: Advanced Robotics. - : Taylor & Francis. - 0169-1864 .- 1568-5535. ; 30:6, s. 402-415
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents an effective integration method of multiple modalities such as depth, color, and reflectance for place categorization. To achieve better performance with integrated multi-modalities, we introduce a novel descriptor, local N-ary patterns (LTP), which can perform robust discrimination of place categorization. In this paper, the LNP descriptor is applied to a combination of two modalities, i.e. depth and reflectance, provided by a laser range finder. However, the LNP descriptor can be easily extended to a larger number of modalities. The proposed LNP describes relationships between the multi-modal values of pixels and their neighboring pixels. Since we consider the multi-modal relationship, our proposed method clearly demonstrates more effective classification results than using individual modalities. We carried out experiments with the Kyushu University Indoor Semantic Place Dataset, which is publicly available. This data-set is composed of five indoor categories: corridors, kitchens, laboratories, study rooms, and offices. We confirmed that our proposed method outperforms previous uni-modal descriptors.
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| 3. |
- Jung, Hojung, et al.
(författare)
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Multi-modal panoramic 3D outdoor datasets for place categorization
- 2016
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Ingår i: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). - : IEEE Press. - 9781509037629 ; , s. 4545-4550
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Konferensbidrag (refereegranskat)abstract
- We present two multi-modal panoramic 3D outdoor (MPO) datasets for semantic place categorization with six categories: forest, coast, residential area, urban area and indoor/outdoor parking lot. The first dataset consists of 650 static panoramic scans of dense (9,000,000 points) 3D color and reflectance point clouds obtained using a FARO laser scanner with synchronized color images. The second dataset consists of 34,200 real-time panoramic scans of sparse (70,000 points) 3D reflectance point clouds obtained using a Velodyne laser scanner while driving a car. The datasets were obtained in the city of Fukuoka, Japan and are publicly available in [1], [2]. In addition, we compare several approaches for semantic place categorization with best results of 96.42% (dense) and 89.67% (sparse).
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| 4. |
- Jung, Hojung, et al.
(författare)
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The Outdoor LiDAR Dataset for Semantic Place Labeling
- 2015
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Ingår i: The Abstracts of the international conference on advanced mechatronics. - Tokyo : Japan Society of Mechanical Engineers. ; , s. 154-155
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Konferensbidrag (refereegranskat)abstract
- We present two sets of outdoor LiDAR dataset for semantic place labeling using two different LiDAR sensors. Recognizing outdoor places according to semantic categories is useful for a mobile service robot, which works adaptively according to the surrounding conditions. However, place recognition is not straight forward due to the wide variety of environments and sensor performance limitations. In this paper, we present two sets of outdoor LiDAR dataset captured by two different LiDAR sensors, SICK and FARO LiDAR sensors. The LiDAR datasets consist of four different semantic places including forest, residential area, parking lot and urban area categories. The datasets are useful for benchmarking vision-based semantic place labeling in outdoor environments.
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| 5. |
- Jung, Hojung, et al.
(författare)
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Two-dimensional local ternary patterns using synchronized images for outdoor place categorization
- 2014
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Ingår i: 2014 IEEE International Conference on Image Processing (ICIP). - : IEEE. - 9781479957514 ; , s. 5726-5730
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Konferensbidrag (refereegranskat)abstract
- We present a novel approach for outdoor place categorization using synchronized texture and depth images obtained using a laser scanner. Categorizing outdoor places according to type is useful for autonomous driving or service robots, which work adaptively according to the surrounding conditions. However, place categorization is not straight forward due to the wide variety of environments and sensor performance limitations. In the present paper, we introduce a two-dimensional local ternary pattern (2D-LTP) descriptor using a pair of synchronized texture and depth images. The proposed 2D-LTP describes the local co-occurrence of a synchronized and complementary image pair with ternary patterns. In the present study, we construct histograms of a 2D-LTP as a feature of an outdoor place and apply singular value decomposition (SVD) to deal with the high dimensionality of the place. The novel descriptor, i.e., the 2D-LTP, exhibits a higher categorization performance than conventional image descriptors with outdoor place experiments.
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| 6. |
- Martinez Mozos, Oscar, 1974-, et al.
(författare)
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Categorization of Indoor Places by Combining Local Binary Pattern Histograms of Range and Reflectance Data from Laser Range Finders
- 2013
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Ingår i: Advanced Robotics. - : Taylor & Francis. - 0169-1864 .- 1568-5535. ; 27:18, s. 1455-1464
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents an approach to categorize typical places in indoor environments using 3D scans provided by a laser range finder. Examples of such places are offices, laboratories, or kitchens. In our method, we combine the range and reflectance data from the laser scan for the final categorization of places. Range and reflectance images are transformed into histograms of local binary patterns and combined into a single feature vector. This vector is later classified using support vector machines. The results of the presented experiments demonstrate the capability of our technique to categorize indoor places with high accuracy. We also show that the combination of range and reflectance information improves the final categorization results in comparison with a single modality.
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| 7. |
- Martinez Mozos, Oscar, 1974-, et al.
(författare)
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Fukuoka datasets for place categorization
- 2019
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Ingår i: The international journal of robotics research. - : Sage Publications. - 0278-3649 .- 1741-3176. ; 38:5, s. 507-517
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents several multi-modal 3D datasets for the problem of categorization of places. In this problem. a robotic agent should decide on the type of place/environment where it is located (residential area, forest, etc.) using information gathered by its sensors. In addition to the 3D depth information, the datasets include additional modalities such as RGB or reflectance images. The observations were taken in different indoor and outdoor environments in Fukuoka city, Japan. Outdoor place categories include forests, urban areas, indoor parking, outdoor parking, coastal areas, and residential areas. Indoor place categories include corridors, offices, study rooms, kitchens, laboratories, and toilets. The datasets are available to download at http://robotics.ait.kyushu-u.ac.jp/kyushu_datasets.
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| 8. |
- Nakashima, Kazuto, et al.
(författare)
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Learning geometric and photometric features from panoramic LiDAR scans for outdoor place categorization
- 2018
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Ingår i: Advanced Robotics. - : Taylor & Francis. - 0169-1864 .- 1568-5535. ; 32:14, s. 750-765
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Tidskriftsartikel (refereegranskat)abstract
- Semantic place categorization, which is one of the essential tasks for autonomous robots and vehicles, allows them to have capabilities of self-decision and navigation in unfamiliar environments. In particular, outdoor places are more difficult targets than indoor ones due to perceptual variations, such as dynamic illuminance over 24 hours and occlusions by cars and pedestrians. This paper presents a novel method of categorizing outdoor places using convolutional neural networks (CNNs), which take omnidirectional depth/reflectance images obtained by 3D LiDARs as the inputs. First, we construct a large-scale outdoor place dataset named Multi-modal Panoramic 3D Outdoor (MPO) comprising two types of point clouds captured by two different LiDARs. They are labeled with six outdoor place categories: coast, forest, indoor/outdoor parking, residential area, and urban area. Second, we provide CNNs for LiDAR-based outdoor place categorization and evaluate our approach with the MPO dataset. Our results on the MPO dataset outperform traditional approaches and show the effectiveness in which we use both depth and reflectance modalities. To analyze our trained deep networks, we visualize the learned features.
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| 9. |
- Nakashima, Kazuto, et al.
(författare)
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Recognizing outdoor scenes by convolutional features of omni-directional LiDAR scans
- 2017
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Ingår i: 2017 IEEE/SICE International Symposium on System Integration (SII). - : IEEE. - 9781538622636 ; , s. 387-392
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Konferensbidrag (refereegranskat)abstract
- We present a novel method for the outdoor scene categorization using 2D convolutional neural networks (CNNs) which take panoramic depth images obtained by a 3D laser scanner as input. We evaluate our approach in two outdoor scene datasets including six categories: coast, forest, indoor parking, outdoor parking, residential area, and urban area. Our results on both datasets (over 94%) outperform previous approaches and show the effectiveness of this approach for outdoor scene categorization using depth images. To analyze our trained networks we visualize the learned features by using two visualization methods.
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