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For the indoor navigation of service robots, human–robot interaction and adapting to the environment still need to be strengthened, including determining the navigation goal socially, improving the success rate of passing doors, and optimizing the path planning efficiency. This paper proposes an indoor navigation system based on object semantic grid and topological map, to optimize the above problems. First, natural language is used as a human–robot interaction form, from which the target room, object, and spatial relationship can be extracted by using speech recognition and word segmentation. Then, the robot selects the goal point from the target space by object affordance theory. To improve the navigation success rate and safety, we generate auxiliary navigation points on both sides of the door to correct the robot trajectory. Furthermore, based on the topological map and auxiliary navigation points, the global path is segmented into each topological area. The path planning algorithm is carried on respectively in every room, which significantly improves the navigation efficiency. This system has demonstrated to support autonomous navigation based on language interaction and significantly improve the safety, efficiency, and robustness of indoor robot navigation. Our system has been successfully tested in real domestic environments.
Jiadong Zhang; Wei Wang; Xianyu Qi; Ziwei Liao. Social and Robust Navigation for Indoor Robots Based on Object Semantic Grid and Topological Map. Applied Sciences 2020, 10, 8991 .
AMA StyleJiadong Zhang, Wei Wang, Xianyu Qi, Ziwei Liao. Social and Robust Navigation for Indoor Robots Based on Object Semantic Grid and Topological Map. Applied Sciences. 2020; 10 (24):8991.
Chicago/Turabian StyleJiadong Zhang; Wei Wang; Xianyu Qi; Ziwei Liao. 2020. "Social and Robust Navigation for Indoor Robots Based on Object Semantic Grid and Topological Map." Applied Sciences 10, no. 24: 8991.
Indoor service robots need to build an object-centric semantic map to understand and execute human instructions. Conventional visual simultaneous localization and mapping (SLAM) systems build a map using geometric features such as points, lines, and planes as landmarks. However, they lack a semantic understanding of the environment. This paper proposes an object-level semantic SLAM algorithm based on RGB-D data, which uses a quadric surface as an object model to compactly represent the object’s position, orientation, and shape. This paper proposes and derives two types of RGB-D camera-quadric observation models: a complete model and a partial model. The complete model combines object detection and point cloud data to estimate a complete ellipsoid in a single RGB-D frame. The partial model is activated when the depth data is severely missing because of illuminations or occlusions, which uses bounding boxes from object detection to constrain objects. Compared with the state-of-the-art quadric SLAM algorithms that use a monocular observation model, the RGB-D observation model reduces the requirements of the observation number and viewing angle changes, which helps improve the accuracy and robustness. This paper introduces a nonparametric pose graph to solve data associations in the back end, and innovatively applies it to the quadric surface model. We thoroughly evaluated the algorithm on two public datasets and an author-collected mobile robot dataset in a home-like environment. We obtained obvious improvements on the localization accuracy and mapping effects compared with two state-of-the-art object SLAM algorithms.
Ziwei Liao; Wei Wang; Xianyu Qi; Xiaoyu Zhang. RGB-D Object SLAM Using Quadrics for Indoor Environments. Sensors 2020, 20, 5150 .
AMA StyleZiwei Liao, Wei Wang, Xianyu Qi, Xiaoyu Zhang. RGB-D Object SLAM Using Quadrics for Indoor Environments. Sensors. 2020; 20 (18):5150.
Chicago/Turabian StyleZiwei Liao; Wei Wang; Xianyu Qi; Xiaoyu Zhang. 2020. "RGB-D Object SLAM Using Quadrics for Indoor Environments." Sensors 20, no. 18: 5150.
Occupied grid maps are sufficient for mobile robots to complete metric navigation tasks in domestic environments. However, they lack semantic information to endow the robots with the ability of social goal selection and human-friendly operation modes. In this paper, we propose an object semantic grid mapping system with 2D Light Detection and Ranging (LiDAR) and RGB-D sensors to solve this problem. At first, we use a laser-based Simultaneous Localization and Mapping (SLAM) to generate an occupied grid map and obtain a robot trajectory. Then, we employ object detection to get an object’s semantics of color images and use joint interpolation to refine camera poses. Based on object detection, depth images, and interpolated poses, we build a point cloud with object instances. To generate object-oriented minimum bounding rectangles, we propose a method for extracting the dominant directions of the room. Furthermore, we build object goal spaces to help the robots select navigation goals conveniently and socially. We have used the [email protected] dataset to verify the system; the verification results show that our system is effective.
Xianyu Qi; Wei Wang; Ziwei Liao; Xiaoyu Zhang; Dongsheng Yang; Ran Wei. Object Semantic Grid Mapping with 2D LiDAR and RGB-D Camera for Domestic Robot Navigation. Applied Sciences 2020, 10, 5782 .
AMA StyleXianyu Qi, Wei Wang, Ziwei Liao, Xiaoyu Zhang, Dongsheng Yang, Ran Wei. Object Semantic Grid Mapping with 2D LiDAR and RGB-D Camera for Domestic Robot Navigation. Applied Sciences. 2020; 10 (17):5782.
Chicago/Turabian StyleXianyu Qi; Wei Wang; Ziwei Liao; Xiaoyu Zhang; Dongsheng Yang; Ran Wei. 2020. "Object Semantic Grid Mapping with 2D LiDAR and RGB-D Camera for Domestic Robot Navigation." Applied Sciences 10, no. 17: 5782.
This article proposes a semantic grid mapping method for domestic robot navigation. Occupancy grid maps are sufficient for mobile robots to complete point-to-point navigation tasks in 2-D small-scale environments. However, when used in the real domestic scene, grid maps are lack of semantic information for end users to specify navigation tasks conveniently. Semantic grid maps, enhancing the occupancy grid map with the semantics of objects and rooms, endowing the robots with the capacity of robust navigation skills and human-friendly operation modes, are thus proposed to overcome this limitation. In our method, an object semantic grid map is built with low-cost sonar and binocular stereovision sensors by correctly fusing the occupancy grid map and object point clouds. Topological spaces of each object are defined to make robots autonomously select navigation destinations. Based on the domestic common sense of the relationship between rooms and objects, topological segmentation is used to get room semantics. Our method is evaluated in a real homelike environment, and the results show that the generated map is at a satisfactory precision and feasible for a domestic mobile robot to complete navigation tasks commanded in natural language with a high success rate.
Xianyu Qi; Wei Wang; Mei Yuan; Yuliang Wang; Mingbo Li; Lin Xue; Yingpin Sun. Building semantic grid maps for domestic robot navigation. International Journal of Advanced Robotic Systems 2020, 17, 1 .
AMA StyleXianyu Qi, Wei Wang, Mei Yuan, Yuliang Wang, Mingbo Li, Lin Xue, Yingpin Sun. Building semantic grid maps for domestic robot navigation. International Journal of Advanced Robotic Systems. 2020; 17 (1):1.
Chicago/Turabian StyleXianyu Qi; Wei Wang; Mei Yuan; Yuliang Wang; Mingbo Li; Lin Xue; Yingpin Sun. 2020. "Building semantic grid maps for domestic robot navigation." International Journal of Advanced Robotic Systems 17, no. 1: 1.
Simultaneous localization and mapping (SLAM) is a fundamental problem for various applications. For indoor environments, planes are predominant features that are less affected by measurement noise. In this paper, we propose a novel point-plane SLAM system using RGB-D cameras. First, we extract feature points from RGB images and planes from depth images. Then plane correspondences in the global map can be found using their contours. Considering the limited size of real planes, we exploit constraints of plane edges. In general, a plane edge is an intersecting line of two perpendicular planes. Therefore, instead of line-based constraints, we calculate and generate supposed perpendicular planes from edge lines, resulting in more plane observations and constraints to reduce estimation errors. To exploit the orthogonal structure in indoor environments, we also add structural (parallel or perpendicular) constraints of planes. Finally, we construct a factor graph using all of these features. The cost functions are minimized to estimate camera poses and global map. We test our proposed system on public RGB-D benchmarks, demonstrating its robust and accurate pose estimation results, compared with other state-of-the-art SLAM systems.
Xiaoyu Zhang; Wei Wang; Xianyu Qi; Ziwei Liao; Ran Wei. Point-Plane SLAM Using Supposed Planes for Indoor Environments. Sensors 2019, 19, 3795 .
AMA StyleXiaoyu Zhang, Wei Wang, Xianyu Qi, Ziwei Liao, Ran Wei. Point-Plane SLAM Using Supposed Planes for Indoor Environments. Sensors. 2019; 19 (17):3795.
Chicago/Turabian StyleXiaoyu Zhang; Wei Wang; Xianyu Qi; Ziwei Liao; Ran Wei. 2019. "Point-Plane SLAM Using Supposed Planes for Indoor Environments." Sensors 19, no. 17: 3795.
As more and more social robots are applied in human-populated environments, they need an affective model to communicate with human beings naturally and believably. In addition, the model should be flexible to be applied in different areas, such as entertainment and education, and can be easily understood and operated by robot designers. To meet these requirements, we propose an affective model including emotions, moods and personality traits for social robots to mimic the affect changes of human beings. Inspired by the Plutchik’s Wheel of Emotions, we first construct an affective space which can simultaneously represent the affective concepts. According to the affective space, the model can be visualized vividly and easily understood. We then describe the interaction among these concepts to change the robot states to make the robot interact with human beings naturally and believably. By tuning the parameters of the model, it can be flexibly applied in different areas. We evaluate the proposed model in simulation and human-robot interaction experiments and the experimental results show that the model is effective.
Xianyu Qi; Wei Wang; Lei Guo; Mingbo Li; Xiaoyu Zhang; Ran Wei. Building a Plutchik’s Wheel Inspired Affective Model for Social Robots. Journal of Bionic Engineering 2019, 16, 209 -221.
AMA StyleXianyu Qi, Wei Wang, Lei Guo, Mingbo Li, Xiaoyu Zhang, Ran Wei. Building a Plutchik’s Wheel Inspired Affective Model for Social Robots. Journal of Bionic Engineering. 2019; 16 (2):209-221.
Chicago/Turabian StyleXianyu Qi; Wei Wang; Lei Guo; Mingbo Li; Xiaoyu Zhang; Ran Wei. 2019. "Building a Plutchik’s Wheel Inspired Affective Model for Social Robots." Journal of Bionic Engineering 16, no. 2: 209-221.
The state-of-the-art visual simultaneous localization and mapping (V-SLAM) systems have high accuracy localization capabilities and impressive mapping effects. However, most of these systems assume that the operating environment is static, thereby limiting their application in the real dynamic world. In this paper, by fusing the information of an RGB-D camera and two encoders that are mounted on a differential-drive robot, we aim to estimate the motion of the robot and construct a static background OctoMap in both dynamic and static environments. A tightly coupled feature-based method is proposed to fuse the two types of information based on the optimization. Dynamic pixels occupied by dynamic objects are detected and culled to cope with dynamic environments. The ability to identify the dynamic pixels on both predefined and undefined dynamic objects is available, which is attributed to the combination of the CPU-based object detection method and a multiview constraint-based approach. We first construct local sub-OctoMaps by using the keyframes and then fuse the sub-OctoMaps into a full OctoMap. This submap-based approach gives the OctoMap the ability to deform, and significantly reduces the map updating time and memory costs. We evaluated the proposed system in various dynamic and static scenes. The results show that our system possesses competitive pose accuracy and high robustness, as well as the ability to construct a clean static OctoMap in dynamic scenes.
Dongsheng Yang; Shusheng Bi; Wei Wang; Chang Yuan; Xianyu Qi; Yueri Cai. DRE-SLAM: Dynamic RGB-D Encoder SLAM for a Differential-Drive Robot. Remote Sensing 2019, 11, 380 .
AMA StyleDongsheng Yang, Shusheng Bi, Wei Wang, Chang Yuan, Xianyu Qi, Yueri Cai. DRE-SLAM: Dynamic RGB-D Encoder SLAM for a Differential-Drive Robot. Remote Sensing. 2019; 11 (4):380.
Chicago/Turabian StyleDongsheng Yang; Shusheng Bi; Wei Wang; Chang Yuan; Xianyu Qi; Yueri Cai. 2019. "DRE-SLAM: Dynamic RGB-D Encoder SLAM for a Differential-Drive Robot." Remote Sensing 11, no. 4: 380.