Y. Tazaki, Kotaro Wada, Hikaru Nagano, Yasutoshi Yokokohji
This paper proposes a robust posegraph optimization (PGO) method for posegraphs with keypoints. In the conventional PGO formulation, a loop constraint is defined between a pair of nodes, whereas in the proposed method, it is defined between a pair of keypoints. In this manner, robust PGO based on switch variables can be realized in a more fine-grained manner. Loop constraint is defined based on the unique geometric property of proximity point, and implemented as a new edge type of the g2o solver. The proposed method is compared with other robust PGO methods using real world data recorded in Nakanoshima Robot Challenge 2021.
{"title":"Robust Posegraph Optimization Using Proximity Points","authors":"Y. Tazaki, Kotaro Wada, Hikaru Nagano, Yasutoshi Yokokohji","doi":"10.20965/jrm.2023.p1480","DOIUrl":"https://doi.org/10.20965/jrm.2023.p1480","url":null,"abstract":"This paper proposes a robust posegraph optimization (PGO) method for posegraphs with keypoints. In the conventional PGO formulation, a loop constraint is defined between a pair of nodes, whereas in the proposed method, it is defined between a pair of keypoints. In this manner, robust PGO based on switch variables can be realized in a more fine-grained manner. Loop constraint is defined based on the unique geometric property of proximity point, and implemented as a new edge type of the g2o solver. The proposed method is compared with other robust PGO methods using real world data recorded in Nakanoshima Robot Challenge 2021.","PeriodicalId":51661,"journal":{"name":"Journal of Robotics and Mechatronics","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138994378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jin Akiyama, Yuan Zong, Naoki Shinada, Taro Suzuki, Y. Amano
In this study, we propose a method for generating highly accurate high-density point clouds of piping facilities using an unmanned aerial vehicle (UAV) laser scanner and a handheld laser scanner. The point cloud for each scanline measured by the UAV scanner is repositioned on the piping axis, and the handheld scanner’s 3D point cloud is subsequently registered so that the center axis of the piping coincides with the UAV point cloud as a reference. The method proposed in this study was used to accurately reconstruct linear piping measured in high winds, which can easily deteriorate measurement accuracy. Whereas the conventional method incurred a deviation of 44.3 mm between the predicted and true values at altitudes of 15 m, the proposed method reduced this deviation to 19.4 mm. An application of the registration method demonstrated that the combined use of the two laser scanners enabled the creation of a high-density point cloud.
{"title":"High-Resolution Point Cloud Registration Method for Three-Dimensional Piping Measurements","authors":"Jin Akiyama, Yuan Zong, Naoki Shinada, Taro Suzuki, Y. Amano","doi":"10.20965/jrm.2023.p1655","DOIUrl":"https://doi.org/10.20965/jrm.2023.p1655","url":null,"abstract":"In this study, we propose a method for generating highly accurate high-density point clouds of piping facilities using an unmanned aerial vehicle (UAV) laser scanner and a handheld laser scanner. The point cloud for each scanline measured by the UAV scanner is repositioned on the piping axis, and the handheld scanner’s 3D point cloud is subsequently registered so that the center axis of the piping coincides with the UAV point cloud as a reference. The method proposed in this study was used to accurately reconstruct linear piping measured in high winds, which can easily deteriorate measurement accuracy. Whereas the conventional method incurred a deviation of 44.3 mm between the predicted and true values at altitudes of 15 m, the proposed method reduced this deviation to 19.4 mm. An application of the registration method demonstrated that the combined use of the two laser scanners enabled the creation of a high-density point cloud.","PeriodicalId":51661,"journal":{"name":"Journal of Robotics and Mechatronics","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138954624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, personal mobility vehicles have been required to operate autonomously in places with numerous pedestrians. A navigation method using a single human-following scheme is used to avoid collision with pedestrians. However, in many cases, a single human-following method cannot be successfully used for guidance. In crowded places, pedestrians do not always keep walking in the desired direction a user wants to go, and the vehicle must change the target pedestrian frequently. Instead of following a single pedestrian, we propose a method for the vehicle to follow a cluster of pedestrians for stable and robust following. First, the pedestrians around the vehicle are detected by multiple RGB-D cameras, and the pedestrians are tracked using YOLO and Deep Sort. Pedestrians are classified according to their walking direction, and the cluster of pedestrians walking toward the goal is selected and followed. However, the position of pedestrian is sometimes lost in occlusions and the accuracy of the walking direction depends on the distance and pose detected by the sensors. A notable problem is that the cluster of pedestrians is unstable in the cluster following; therefore, a median of candidate vectors (MCV) observer is used to remove outliers caused by observation errors. The proposed method is applied to a scenario involving pedestrians walking toward an elevator hall in a building, and its effectiveness is verified through experiments.
{"title":"Navigation System for Personal Mobility Vehicles Following a Cluster of Pedestrians in a Corridor Using Median of Candidate Vectors Observer","authors":"N. Matsunaga, Ikuo Yamamoto, Hiroshi Okajima","doi":"10.20965/jrm.2023.p1562","DOIUrl":"https://doi.org/10.20965/jrm.2023.p1562","url":null,"abstract":"In recent years, personal mobility vehicles have been required to operate autonomously in places with numerous pedestrians. A navigation method using a single human-following scheme is used to avoid collision with pedestrians. However, in many cases, a single human-following method cannot be successfully used for guidance. In crowded places, pedestrians do not always keep walking in the desired direction a user wants to go, and the vehicle must change the target pedestrian frequently. Instead of following a single pedestrian, we propose a method for the vehicle to follow a cluster of pedestrians for stable and robust following. First, the pedestrians around the vehicle are detected by multiple RGB-D cameras, and the pedestrians are tracked using YOLO and Deep Sort. Pedestrians are classified according to their walking direction, and the cluster of pedestrians walking toward the goal is selected and followed. However, the position of pedestrian is sometimes lost in occlusions and the accuracy of the walking direction depends on the distance and pose detected by the sensors. A notable problem is that the cluster of pedestrians is unstable in the cluster following; therefore, a median of candidate vectors (MCV) observer is used to remove outliers caused by observation errors. The proposed method is applied to a scenario involving pedestrians walking toward an elevator hall in a building, and its effectiveness is verified through experiments.","PeriodicalId":51661,"journal":{"name":"Journal of Robotics and Mechatronics","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138955086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Self-localization in probabilistic robotics requires detailed, geographically consistent environmental maps, which increases the computational cost. In this study, we propose a simple self-localization method that does not require such maps. In the proposed method, the order structure, such as the mobile robot’s navigation route, is embedded as trajectory attractors in the state space of a nonmonotone neural network, and self-position estimation is performed by processing based on the autonomous dynamics of the network. From experiments, we demonstrated the basic performance of the proposed method, including robust self-localization in complex outdoor environments. Furthermore, self-localization is possible on multiple courses with overlapping paths by suitably varying the network dynamics based on environmental information. While issues remain, this study points to the great potential of neurodynamics-based robotic self-localization.
{"title":"Self-Localization Using Trajectory Attractors in Outdoor Environments","authors":"Ken Yamane, Mitsunori Akutsu","doi":"10.20965/jrm.2023.p1435","DOIUrl":"https://doi.org/10.20965/jrm.2023.p1435","url":null,"abstract":"Self-localization in probabilistic robotics requires detailed, geographically consistent environmental maps, which increases the computational cost. In this study, we propose a simple self-localization method that does not require such maps. In the proposed method, the order structure, such as the mobile robot’s navigation route, is embedded as trajectory attractors in the state space of a nonmonotone neural network, and self-position estimation is performed by processing based on the autonomous dynamics of the network. From experiments, we demonstrated the basic performance of the proposed method, including robust self-localization in complex outdoor environments. Furthermore, self-localization is possible on multiple courses with overlapping paths by suitably varying the network dynamics based on environmental information. While issues remain, this study points to the great potential of neurodynamics-based robotic self-localization.","PeriodicalId":51661,"journal":{"name":"Journal of Robotics and Mechatronics","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138955165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Ohya, Koichi Ozaki, Tomohito Takubo, Shin’ichi Yuta, Yoshihiro Takita
The Tsukuba Challenge and the Nakanoshima Robot Challenge are both technical challenges in which mobile robots run autonomously in real outdoor environments. They have been held almost every year since 2007 and 2018, respectively, and many robots have participated in these public experiments. The autonomous navigation of a mobile robot in the real world, that is, not on test tracks but in environments normally used by everyday people, poses a great many challenges. The robots have to deal with environments that change with the time of day, the weather, the season, etc., and they have to deal with unexpected stationary obstacles as well as moving ones, including people, bicycles, etc. These days, demonstration tests of delivery robots are being conducted in many places, but there are still many problems that need to be solved. In this special issue, we have gathered papers detailing the insights gained from running mobile robots in the two outdoor experiments, the Tsukuba Challenge and the Nakanoshima Robot Challenge. To run a robot with a high success rate in a real environment, it is very important to devise the robot configuration, the sensor data processing, and the behavior control based on the knowledge gained from many experiences. We hope that sharing the successes and failures in the papers in this special issue will lead to further technological improvements in the future.
{"title":"Special Issue on Autonomous Robotics Challenge","authors":"A. Ohya, Koichi Ozaki, Tomohito Takubo, Shin’ichi Yuta, Yoshihiro Takita","doi":"10.20965/jrm.2023.p1405","DOIUrl":"https://doi.org/10.20965/jrm.2023.p1405","url":null,"abstract":"The Tsukuba Challenge and the Nakanoshima Robot Challenge are both technical challenges in which mobile robots run autonomously in real outdoor environments. They have been held almost every year since 2007 and 2018, respectively, and many robots have participated in these public experiments. The autonomous navigation of a mobile robot in the real world, that is, not on test tracks but in environments normally used by everyday people, poses a great many challenges. The robots have to deal with environments that change with the time of day, the weather, the season, etc., and they have to deal with unexpected stationary obstacles as well as moving ones, including people, bicycles, etc. These days, demonstration tests of delivery robots are being conducted in many places, but there are still many problems that need to be solved. In this special issue, we have gathered papers detailing the insights gained from running mobile robots in the two outdoor experiments, the Tsukuba Challenge and the Nakanoshima Robot Challenge. To run a robot with a high success rate in a real environment, it is very important to devise the robot configuration, the sensor data processing, and the behavior control based on the knowledge gained from many experiences. We hope that sharing the successes and failures in the papers in this special issue will lead to further technological improvements in the future.","PeriodicalId":51661,"journal":{"name":"Journal of Robotics and Mechatronics","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138955732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper outlines a path planning method for autonomous rovers navigating urban environments without prior mapping, with a particular focus on addressing the Tsukuba Challenge. Our approach utilizes observations of pedestrian and robot movement trajectories to construct path graphs for global path planning. We provide a detailed overview of the autonomous rover’s hardware and software system, as well as a comprehensive description of the path planning algorithm. Our methodology entails extracting and continuously tracking dynamic objects from LiDAR data, resulting in the creation of a path graph based on their observed trajectories. Subsequently, a path aligned with the desired direction is selected. Notably, in indoor experimental settings, our approach proves effective, as the rover successfully generates a path to the goal by closely monitoring and tracking pedestrian movements. In conclusion, this paper introduces a promising path planning methodology and suggests potential areas for further research in autonomous mobility within uncharted environments.
{"title":"Path Planning Using a Flow of Pedestrian Traffic in an Unknown Environment","authors":"Kiichiro Ishikawa, Kei Otomo, Hayato Osaki, Taiga Odaka","doi":"10.20965/jrm.2023.p1460","DOIUrl":"https://doi.org/10.20965/jrm.2023.p1460","url":null,"abstract":"This paper outlines a path planning method for autonomous rovers navigating urban environments without prior mapping, with a particular focus on addressing the Tsukuba Challenge. Our approach utilizes observations of pedestrian and robot movement trajectories to construct path graphs for global path planning. We provide a detailed overview of the autonomous rover’s hardware and software system, as well as a comprehensive description of the path planning algorithm. Our methodology entails extracting and continuously tracking dynamic objects from LiDAR data, resulting in the creation of a path graph based on their observed trajectories. Subsequently, a path aligned with the desired direction is selected. Notably, in indoor experimental settings, our approach proves effective, as the rover successfully generates a path to the goal by closely monitoring and tracking pedestrian movements. In conclusion, this paper introduces a promising path planning methodology and suggests potential areas for further research in autonomous mobility within uncharted environments.","PeriodicalId":51661,"journal":{"name":"Journal of Robotics and Mechatronics","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138956961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Ratsamee, Pudit Tempattarachoke, Laphonchai Jirachuphun, Masafumi Miwa, K. Somprasong
This paper presents a multi-box interpolation method to estimate point clouds during aerial-aquatic transition. Our proposed method is developed based on an investigation of noise characteristics of aerial point clouds and aquatic point clouds. To evaluate the performance of realistic point cloud estimation, we compare the interpolation method with the Gaussian mixture method. We also investigate how single-box and multi-box approaches deal with noise in point cloud estimation. The simulation and the experimental results show that the estimated point cloud is accurate even when the aerial and aquatic point clouds contain noise. Also, the multi-box concept helps the algorithm to avoid taking unwanted noise into consideration when predicting point clouds.
{"title":"Point Cloud Estimation During Aerial-Aquatic Transition in Monocular Camera-Based Localization and Mapping","authors":"P. Ratsamee, Pudit Tempattarachoke, Laphonchai Jirachuphun, Masafumi Miwa, K. Somprasong","doi":"10.20965/jrm.2023.p1645","DOIUrl":"https://doi.org/10.20965/jrm.2023.p1645","url":null,"abstract":"This paper presents a multi-box interpolation method to estimate point clouds during aerial-aquatic transition. Our proposed method is developed based on an investigation of noise characteristics of aerial point clouds and aquatic point clouds. To evaluate the performance of realistic point cloud estimation, we compare the interpolation method with the Gaussian mixture method. We also investigate how single-box and multi-box approaches deal with noise in point cloud estimation. The simulation and the experimental results show that the estimated point cloud is accurate even when the aerial and aquatic point clouds contain noise. Also, the multi-box concept helps the algorithm to avoid taking unwanted noise into consideration when predicting point clouds.","PeriodicalId":51661,"journal":{"name":"Journal of Robotics and Mechatronics","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139169432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Human-robot collaboration has garnered significant attention in the manufacturing industry due to its potential for optimizing the strengths of both human operators and robots. In this study, we present a novel variable admittance control method based on iterative learning for collaborative manipulation, aiming to enhance operational performance. This proposed method enables the adjustment of admittance to meet task requirements without the need for heuristic designs of admittance modulation strategies. Furthermore, the incorporation of dynamic time warping in human operational detection assists in mitigating the learning performance decline caused by fluctuations in human operations. To validate the effectiveness of our approach, we conducted extensive experiments. The results of these experiments highlight that the proposed method enhances human-robot collaborative manipulation performance compared to conventional methods. This approach also exhibits the potential for addressing complex tasks that are typically influenced by diverse human factors, including skill level and intention.
{"title":"Learning Variable Admittance Control for Human-Robot Collaborative Manipulation","authors":"T. Yamawaki, Liem Duc Tran, M. Yashima","doi":"10.20965/jrm.2023.p1593","DOIUrl":"https://doi.org/10.20965/jrm.2023.p1593","url":null,"abstract":"Human-robot collaboration has garnered significant attention in the manufacturing industry due to its potential for optimizing the strengths of both human operators and robots. In this study, we present a novel variable admittance control method based on iterative learning for collaborative manipulation, aiming to enhance operational performance. This proposed method enables the adjustment of admittance to meet task requirements without the need for heuristic designs of admittance modulation strategies. Furthermore, the incorporation of dynamic time warping in human operational detection assists in mitigating the learning performance decline caused by fluctuations in human operations. To validate the effectiveness of our approach, we conducted extensive experiments. The results of these experiments highlight that the proposed method enhances human-robot collaborative manipulation performance compared to conventional methods. This approach also exhibits the potential for addressing complex tasks that are typically influenced by diverse human factors, including skill level and intention.","PeriodicalId":51661,"journal":{"name":"Journal of Robotics and Mechatronics","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138954426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we propose an inchworm-type soft robot using a self-healing gel as its body and shape-memory alloy (SMA) wires as its actuators. To realize inchworm-like locomotion, two coiled SMA wires are placed in parallel in the gel-fabricated body. The bottom-side wire and the upper-side wire reciprocally bend by applying electric current to the actuators. To realize the self-restoration automatically, the robot consists of a self-healing body equipped with magnets. The paper introduces the structure of the inchworm-shaped robot with its inchworm-like locomotion performance, together with the self-healing function.
在本文中，我们提出了一种以自愈合凝胶为主体、形状记忆合金（SMA）丝为执行器的尺蠖型软体机器人。为了实现类似尺蠖的运动，我们在凝胶制造的本体中平行放置了两根盘绕的 SMA 线。通过向致动器施加电流，下侧线和上侧线会相互弯曲。为了实现自动修复，机器人由装有磁铁的自修复体组成。本文介绍了具有类似尺蠖运动性能的尺蠖形机器人的结构以及自修复功能。
{"title":"An Inchworm Robot with Self-Healing Ability Using SMA Actuators","authors":"Haruya Fukuchi, Hideyuki Sawada","doi":"10.20965/jrm.2023.p1615","DOIUrl":"https://doi.org/10.20965/jrm.2023.p1615","url":null,"abstract":"In this paper, we propose an inchworm-type soft robot using a self-healing gel as its body and shape-memory alloy (SMA) wires as its actuators. To realize inchworm-like locomotion, two coiled SMA wires are placed in parallel in the gel-fabricated body. The bottom-side wire and the upper-side wire reciprocally bend by applying electric current to the actuators. To realize the self-restoration automatically, the robot consists of a self-healing body equipped with magnets. The paper introduces the structure of the inchworm-shaped robot with its inchworm-like locomotion performance, together with the self-healing function.","PeriodicalId":51661,"journal":{"name":"Journal of Robotics and Mechatronics","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138954741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We are exploring the use of semantic scene understanding in autonomous navigation for the Tsukuba Challenge. However, manually creating a comprehensive dataset that covers various outdoor scenes with time and weather variations to ensure high accuracy in semantic segmentation is onerous. Therefore, we propose modifications to the model and backbone of semantic segmentation, along with data augmentation techniques. The data augmentation techniques, including the addition of virtual shadows, histogram matching, and style transformations, aim to improve the representation of variations in shadow presence and color tones. In our evaluation using images from the Tsukuba Challenge course, we achieved the highest accuracy by switching the model to PSPNet and changing the backbone to ResNeXt. Furthermore, the adaptation of shadow and histogram proved effective for critical classes in robot navigation, such as road, sidewalk, and terrain. In particular, the combination of histogram matching and shadow application demonstrated effectiveness for data not included in the base training dataset.
{"title":"Data Augmentation for Semantic Segmentation Using a Real Image Dataset Captured Around the Tsukuba City Hall","authors":"Yuriko Ueda, Miho Adachi, Junya Morioka, Marin Wada, Ryusuke Miyamoto","doi":"10.20965/jrm.2023.p1450","DOIUrl":"https://doi.org/10.20965/jrm.2023.p1450","url":null,"abstract":"We are exploring the use of semantic scene understanding in autonomous navigation for the Tsukuba Challenge. However, manually creating a comprehensive dataset that covers various outdoor scenes with time and weather variations to ensure high accuracy in semantic segmentation is onerous. Therefore, we propose modifications to the model and backbone of semantic segmentation, along with data augmentation techniques. The data augmentation techniques, including the addition of virtual shadows, histogram matching, and style transformations, aim to improve the representation of variations in shadow presence and color tones. In our evaluation using images from the Tsukuba Challenge course, we achieved the highest accuracy by switching the model to PSPNet and changing the backbone to ResNeXt. Furthermore, the adaptation of shadow and histogram proved effective for critical classes in robot navigation, such as road, sidewalk, and terrain. In particular, the combination of histogram matching and shadow application demonstrated effectiveness for data not included in the base training dataset.","PeriodicalId":51661,"journal":{"name":"Journal of Robotics and Mechatronics","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138955363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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