Real-Time Dense Construction With Deep Multiview Stereo Using Camera and IMU Sensors

Liu, Yanjie; H, Wu; Wang, Chao; Wei, Yanlong; Ren, Mingfa; Feng, Tong

doi:10.1109/jsen.2023.3295000

Cited by 11 publications

(7 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, it is necessary to adjust the velocity posted by the controller to make it more suitable for robot-motion execution, which is more like trajectory tracking that the DWA algorithm does not have. Furthermore, how to add vision sensors [36] to assist the robot in performing better dynamic-obstacle avoidance and target tracking is also an issue worthy of in-depth research.…”

Section: Discussionmentioning

confidence: 99%

Mobile Robot Path Planning Based on Kinematically Constrained A-Star Algorithm and DWA Fusion Algorithm

Liu,

Wang,

et al. 2023

Mathematics

Self Cite

View full text Add to dashboard Cite

Path-planning research has been the key to mobile-robot-navigation technology. However, traditional path-planning algorithms have some shortcomings. To solve these problems, this paper proposes a fusion algorithm that combines the kinematical constrained A* algorithm with the Dynamic Window Approach (DWA) algorithm. The kinematical constrained A* algorithm can plan the global path, and then the DWA algorithm can plan the local path under the global path’s guidance. Firstly, combined with robot kinematics, we improve the node-expansion method and heuristic-function model of the A* algorithm, which improves the search efficiency, reduces the number of path bends and lowers the computational cost so that the path generated by the A* algorithm better meets the needs of robot motion. Secondly, we optimize the trajectory-evaluation function of the DWA algorithm so that the local paths planned by the DWA algorithm are smoother and more coherent, which is easier for robot-motion execution. Finally, we extract the key nodes from the global path planned by the A* algorithm to guide the DWA algorithm for local path planning and dynamic-obstacle avoidance and to make the local path closer to the global path. Through simulation and practical experiments, the effectiveness of the fusion algorithm proposed in this paper is verified.

show abstract

Section: Discussionmentioning

confidence: 99%

Mobile Robot Path Planning Based on Kinematically Constrained A-Star Algorithm and DWA Fusion Algorithm

Liu,

Wang,

et al. 2023

Mathematics

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the construction of dense point cloud maps, the realtime 3D reconstruction model proposed in literature [43] cleverly fuses visual and inertial sensors, significantly enhancing the accuracy and stability of the reconstruction. Moreover, this model also combines static stereo optimization technique and direct visual-inertial odometry, successfully addressing the scale uncertainty problem faced when building dense maps with monocular cameras.…”

Section: Related Workmentioning

confidence: 99%

OMS-SLAM: dynamic scene visual SLAM based on object detection with multiple geometric feature constraints and statistical threshold segmentation

Tang,

Feng,

Liao

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

Simultaneous localization and mapping (SLAM) technology is crucial to robot navigation. Despite the good performance of traditional SLAM algorithms in static environments, dynamic objects typically exist in realistic operating environments. These objects can lead to misassociated features, which in turn considerably impact the system’s localization accuracy and robustness. To better address this challenge, we have proposed the OMS-SLAM. In OMS-SLAM, we adopted the YOLOv8 target detection network to extract object information from environment and designed a dynamic probability propagation model that is coupled with target detection and multiple geometric constrains to determine the dynamic objects in the environment. For the identified dynamic objects, we have designed a foreground image segmentation algorithm based on depth image histogram statistics to extract the object contours and eliminate the feature points within these contours. We then use the grid-based motion statistics matching pair as the filtering strategy to enhance the quality of the feature points and use the enhanced feature points for tracking. This combined method can accurately identify dynamic objects and extract related feature points, significantly reducing its interference and consequently enhancing the system’s robustness and localization accuracy. We also built static dense point cloud maps to support advanced tasks of robots. Finally, through testing on the high-speed dataset of TUM RGB-D, it was found that the root mean square error of the absolute trajectory error in this study decreased by an average of 97.10%, compared to ORB-SLAM2. Moreover, tests in real-world scenarios also confirmed the effectiveness of the OMS-SLAM algorithm in dynamic environments.

show abstract

“…References [13,14] and others used Pointnet++ [15] as a backbone network to learn point set features from local to global. The continuous development of real-time 3D reconstruction [16] offers the potential to understand detailed semantic scenes and brings some new approaches to those grasp detection by understanding object models. Yang et al [17] combined 3D reconstruction with grasp detection to enable the robot to infer the shape of objects and accurately grasp known and unknown objects even when the grasp position is invisible.…”

Section: Introductionmentioning

confidence: 99%

FastGNet: an efficient 6-DOF grasp detection method with multi-attention mechanisms and point transformer network

Ding,

Wang,

Gao

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

A pivotal technology for autonomous robot grasping is efficient and accurate grasp pose detection, which enables robotic arms to grasp objects in cluttered environments without human intervention. However, most existing methods rely on PointNet or CNN as backbones for grasp pose prediction, which may lead to unnecessary computational overhead on invalid grasp points or background information. Consequently, performing efficient grasp pose detection for graspable points in complex scenes becomes a challenge. In this paper, we propose FastGNet, an end-to-end model that combines multiple attention mechanisms with the Transformer architecture to generate 6-DOF grasp poses efficiently. Our approach involves a novel sparse point cloud voxelization technique, preserving the complete mapping between points and voxels while generating positional embeddings for the Transformer network. By integrating unsupervised and supervised attention mechanisms into the grasp model, our method significantly improves the performance of focusing on graspable target points in complex scenes. The effectiveness of FastGNet is validated on the large-scale GraspNet-1Billion dataset. Our approach outperforms previous methods and achieves relatively fast inference times, highlighting its potential to advance autonomous robot grasping capabilities.

show abstract

Real-Time Dense Construction With Deep Multiview Stereo Using Camera and IMU Sensors

Cited by 11 publications

References 39 publications

Mobile Robot Path Planning Based on Kinematically Constrained A-Star Algorithm and DWA Fusion Algorithm

Mobile Robot Path Planning Based on Kinematically Constrained A-Star Algorithm and DWA Fusion Algorithm

OMS-SLAM: dynamic scene visual SLAM based on object detection with multiple geometric feature constraints and statistical threshold segmentation

FastGNet: an efficient 6-DOF grasp detection method with multi-attention mechanisms and point transformer network

Contact Info

Product

Resources

About