scholarly journals DMS-SLAM: A General Visual SLAM System for Dynamic Scenes with Multiple Sensors

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3714 ◽  
Author(s):  
Guihua Liu ◽  
Weilin Zeng ◽  
Bo Feng ◽  
Feng Xu
Keyword(s):  
Large Deviation ◽  
Autonomous Driving ◽  
Position Estimation ◽  
Visual Slam ◽  
Dynamic Scenes ◽  
Current Frame ◽  
Multiple Sensors ◽  
Pose Tracking ◽  
Dynamic Objects ◽  
Local Map

Presently, although many impressed SLAM systems have achieved exceptional accuracy in a real environment, most of them are verified in the static environment. However, for mobile robots and autonomous driving, the dynamic objects in the scene can result in tracking failure or large deviation during pose estimation. In this paper, a general visual SLAM system for dynamic scenes with multiple sensors called DMS-SLAM is proposed. First, the combination of GMS and sliding window is used to achieve the initialization of the system, which can eliminate the influence of dynamic objects and construct a static initialization 3D map. Then, the corresponding 3D points of the current frame in the local map are obtained by reprojection. These points are combined with the constant speed model or reference frame model to achieve the position estimation of the current frame and the update of the 3D map points in the local map. Finally, the keyframes selected by the tracking module are combined with the GMS feature matching algorithm to add static 3D map points to the local map. DMS-SLAM implements pose tracking, closed-loop detection and relocalization based on static 3D map points of the local map and supports monocular, stereo and RGB-D visual sensors in dynamic scenes. Exhaustive evaluation in public TUM and KITTI datasets demonstrates that DMS-SLAM outperforms state-of-the-art visual SLAM systems in accuracy and speed in dynamic scenes.

scholarly journals DM-SLAM: A Feature-Based SLAM System for Rigid Dynamic Scenes

2020 ◽  
Vol 9 (4) ◽  
pp. 202
Author(s):  
Junhao Cheng ◽  
Zhi Wang ◽  
Hongyan Zhou ◽  
Li Li ◽  
Jian Yao
Keyword(s):  
Motion Estimation ◽  
Semantic Segmentation ◽  
Dynamic Environments ◽  
Depth Information ◽  
Dynamic Feature ◽  
Visual Slam ◽  
Feature Points ◽  
Current Frame ◽  
Feature Based ◽  
Dynamic Objects

Most Simultaneous Localization and Mapping (SLAM) methods assume that environments are static. Such a strong assumption limits the application of most visual SLAM systems. The dynamic objects will cause many wrong data associations during the SLAM process. To address this problem, a novel visual SLAM method that follows the pipeline of feature-based methods called DM-SLAM is proposed in this paper. DM-SLAM combines an instance segmentation network with optical flow information to improve the location accuracy in dynamic environments, which supports monocular, stereo, and RGB-D sensors. It consists of four modules: semantic segmentation, ego-motion estimation, dynamic point detection and a feature-based SLAM framework. The semantic segmentation module obtains pixel-wise segmentation results of potentially dynamic objects, and the ego-motion estimation module calculates the initial pose. In the third module, two different strategies are presented to detect dynamic feature points for RGB-D/stereo and monocular cases. In the first case, the feature points with depth information are reprojected to the current frame. The reprojection offset vectors are used to distinguish the dynamic points. In the other case, we utilize the epipolar constraint to accomplish this task. Furthermore, the static feature points left are fed into the fourth module. The experimental results on the public TUM and KITTI datasets demonstrate that DM-SLAM outperforms the standard visual SLAM baselines in terms of accuracy in highly dynamic environments.

scholarly journals Object-Level Semantic Map Construction for Dynamic Scenes

2021 ◽  
Vol 11 (2) ◽  
pp. 645
Author(s):  
Xujie Kang ◽  
Jing Li ◽  
Xiangtao Fan ◽  
Hongdeng Jian ◽  
Chen Xu
Keyword(s):  
Moving Objects ◽  
Dynamic Environments ◽  
Dynamic Object ◽  
Dynamic Scenes ◽  
Current Frame ◽  
Semantic Map ◽  
Pose Tracking ◽  
Camera Pose ◽  
Object Level ◽  
Instance Segmentation

Visual simultaneous localization and mapping (SLAM) is challenging in dynamic environments as moving objects can impair camera pose tracking and mapping. This paper introduces a method for robust dense bject-level SLAM in dynamic environments that takes a live stream of RGB-D frame data as input, detects moving objects, and segments the scene into different objects while simultaneously tracking and reconstructing their 3D structures. This approach provides a new method of dynamic object detection, which integrates prior knowledge of the object model database constructed, object-oriented 3D tracking against the camera pose, and the association between the instance segmentation results on the current frame data and an object database to find dynamic objects in the current frame. By leveraging the 3D static model for frame-to-model alignment, as well as dynamic object culling, the camera motion estimation reduced the overall drift. According to the camera pose accuracy and instance segmentation results, an object-level semantic map representation was constructed for the world map. The experimental results obtained using the TUM RGB-D dataset, which compares the proposed method to the related state-of-the-art approaches, demonstrating that our method achieves similar performance in static scenes and improved accuracy and robustness in dynamic scenes.

scholarly journals Fusion of Multiple Lidars and Inertial Sensors for the Real-Time Pose Tracking of Human Motion

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5342
Author(s):  
Ashok Kumar Patil ◽  
Adithya Balasubramanyam ◽  
Jae Yeong Ryu ◽  
Pavan Kumar B N ◽  
Bharatesh Chakravarthi ◽  
...  
Keyword(s):  
Real Time ◽  
Inertial Sensors ◽  
Tracking System ◽  
Initial Step ◽  
Human Motion ◽  
Position Estimation ◽  
Measurement Unit ◽  
Multiple Sensors ◽  
Pose Tracking ◽  
Motion Activity

Today, enhancement in sensing technology enables the use of multiple sensors to track human motion/activity precisely. Tracking human motion has various applications, such as fitness training, healthcare, rehabilitation, human-computer interaction, virtual reality, and activity recognition. Therefore, the fusion of multiple sensors creates new opportunities to develop and improve an existing system. This paper proposes a pose-tracking system by fusing multiple three-dimensional (3D) light detection and ranging (lidar) and inertial measurement unit (IMU) sensors. The initial step estimates the human skeletal parameters proportional to the target user’s height by extracting the point cloud from lidars. Next, IMUs are used to capture the orientation of each skeleton segment and estimate the respective joint positions. In the final stage, the displacement drift in the position is corrected by fusing the data from both sensors in real time. The installation setup is relatively effortless, flexible for sensor locations, and delivers results comparable to the state-of-the-art pose-tracking system. We evaluated the proposed system regarding its accuracy in the user’s height estimation, full-body joint position estimation, and reconstruction of the 3D avatar. We used a publicly available dataset for the experimental evaluation wherever possible. The results reveal that the accuracy of height and the position estimation is well within an acceptable range of ±3–5 cm. The reconstruction of the motion based on the publicly available dataset and our data is precise and realistic.

Head Pose Tracking Method Based on Face Detection And Stereo Visual SLAM

2021 ◽  
Author(s):  
Suibin Huang ◽  
Hua Xiao ◽  
Peng Han ◽  
Jian Qiu ◽  
Li Peng ◽  
...  
Keyword(s):  
Face Detection ◽  
Visual Slam ◽  
Head Pose ◽  
Tracking Method ◽  
Pose Tracking

scholarly journals A new head pose tracking method based on stereo visual SLAM

2022 ◽  
Vol 82 ◽  
pp. 103402
Author(s):  
Suibin Huang ◽  
Kun Yang ◽  
Hua Xiao ◽  
Peng Han ◽  
Jian Qiu ◽  
...  
Keyword(s):  
Visual Slam ◽  
Head Pose ◽  
Tracking Method ◽  
Pose Tracking

Rapid position estimation and tracking for autonomous driving

2012 ◽  
Author(s):  
Patrick K. Wang ◽  
Peter A. Torrione ◽  
Leslie M. Collins ◽  
Kenneth D. Morton
Keyword(s):  
Autonomous Driving ◽  
Position Estimation

scholarly journals LIO-CSI: LiDAR inertial odometry with loop closure combined with semantic information

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261053
Author(s):  
Gang Wang ◽  
Saihang Gao ◽  
Han Ding ◽  
Hao Zhang ◽  
Hongmin Cai
Keyword(s):  
Point Cloud ◽  
Semantic Information ◽  
Feature Matching ◽  
Autonomous Driving ◽  
Geometric Feature ◽  
Semantic Features ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Front End ◽  
Dynamic Objects

Accurate and reliable state estimation and mapping are the foundation of most autonomous driving systems. In recent years, researchers have focused on pose estimation through geometric feature matching. However, most of the works in the literature assume a static scenario. Moreover, a registration based on a geometric feature is vulnerable to the interference of a dynamic object, resulting in a decline of accuracy. With the development of a deep semantic segmentation network, we can conveniently obtain the semantic information from the point cloud in addition to geometric information. Semantic features can be used as an accessory to geometric features that can improve the performance of odometry and loop closure detection. In a more realistic environment, semantic information can filter out dynamic objects in the data, such as pedestrians and vehicles, which lead to information redundancy in generated map and map-based localization failure. In this paper, we propose a method called LiDAR inertial odometry (LIO) with loop closure combined with semantic information (LIO-CSI), which integrates semantic information to facilitate the front-end process as well as loop closure detection. First, we made a local optimization on the semantic labels provided by the Sparse Point-Voxel Neural Architecture Search (SPVNAS) network. The optimized semantic information is combined into the front-end process of tightly-coupled light detection and ranging (LiDAR) inertial odometry via smoothing and mapping (LIO-SAM), which allows us to filter dynamic objects and improve the accuracy of the point cloud registration. Then, we proposed a semantic assisted scan-context method to improve the accuracy and robustness of loop closure detection. The experiments were conducted on an extensively used dataset KITTI and a self-collected dataset on the Jilin University (JLU) campus. The experimental results demonstrate that our method is better than the purely geometric method, especially in dynamic scenarios, and it has a good generalization ability.

scholarly journals Applications of Cognitive Intelligence in the Information Retrieval Process and Associated Challenges

2021 ◽  
Vol 15 (1) ◽  
pp. 26-38
Author(s):  
Mamata Rath ◽  
Joel J. P. C. Rodrigues ◽  
George S. Oreku
Keyword(s):  
Information Retrieval ◽  
Relevant Information ◽  
Cognitive Models ◽  
Position Estimation ◽  
Retrieval Process ◽  
Dynamic Scenes ◽  
Cognitive Intelligence ◽  
Objective State ◽  
Technology Domain

Information retrieval refers to a noteworthy system of identifying relevant information and recovering it through specific procedures from stored system. These technique is used in many differentiated applications that deal with subjective intelligence. Applications based on information retrieval are identified with various issues, for example, in technology domain, the sudden size changes of the objectives as they approach the sensor. If not taken care of appropriately, the altered changes can present substantial issues in information affiliation and position estimation. Under such a system, the meaning of the objective state is the fundamental advance for programmed comprehension of dynamic scenes. This is the reason of requirement of cognitive models for information retrieval. The existent models move around the connection between data list terms and records.

Robot Localization Using Fuzzy Logic

2005 ◽  
Author(s):  
Avinash G. Dharne ◽  
Suhada Jayasuriya
Keyword(s):  
Fuzzy Logic ◽  
Grid Point ◽  
Computational Cost ◽  
Computation Time ◽  
Position Estimation ◽  
Fuzzy Rules ◽  
Estimation Methods ◽  
Robot Localization ◽  
Multiple Sensors ◽  
Grid Points

Robot Localization is an issue of vital importance for the functioning of autonomous mobile robots. Location information, allows a robot to navigate complex environments and perform local tasks successfully. In mobile sensor networks, this information facilitates important functions like topology control, collision avoidance and development and security of routing protocols. This issue can be divided into the problems of global position estimation, and once that is achieved, of local position tracking. To tackle these, two distinct methods have been used in the past. One is the use of specialized hardware and another is the use of probabilistic Bayesian estimation methods. This paper proposes the use of Fuzzy Logic to tackle this problem. Fuzzy Logic allows us to do away with strict probabilistic rules and to set up heuristic fuzzy rules. It also reduces computation time. A grid-based map is used to describe the environment of the robot and the robot’s confidence in it’s position at each grid-point is determined using sensor measurements. In case the robot is receiving information from multiple sensors, this paper demonstrates the robustness of the scheme to inaccurate sensor information or robot confidence within practical limits. This paper also applies the fuzzy rules to track the robot’s position as it moves. In order to reduce computational cost, this paper proposes limiting the computation of confidences to significant grid-points only.

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