Dense RGB-D SLAM for Humanoid Robots in the Dynamic Humans Environment

Zhang, Tianwei; Uchiyama, Emiko; Nakamura, Yoshihiko

doi:10.1109/humanoids.2018.8625019

Cited by 12 publications

(6 citation statements)

References 17 publications

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“…The absolute trajectory error is mainly evaluated by calculating indicators such as the root mean square error (RMSE), mean error (mean), median error (median), and standard deviation (STD). RMSE can reflect the difference between the real value and the observed value, and STD can reflect the dispersion between the trajectory estimated by the camera and the real trajectory, demonstrating the robustness and stability of the system, which can adequately reflect SLAM performance (de Croon et al, 2021; Zhang et al, 2018).…”

Section: Experiments and Results Analysismentioning

confidence: 99%

YOLOv4‐tiny‐based robust RGB‐D SLAM approach with point and surface feature fusion in complex indoor environments

Chang

2022

Journal of Field Robotics

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Algorithm frameworks based on feature point matching are mature and widely used in simultaneous localization and mapping (SLAM). However, in the complex and changeable indoor environment, feature point matching-based SLAM currently has two major problems, namely, decreased accuracy of pose estimation due to the interference caused by dynamic objects to the SLAM system and tracking loss caused by the lack of feature points in weak texture scenes. To address these problems, herein, we present a robust and real-time RGB-D SLAM algorithm that is based on ORBSLAM3. For interference caused by indoor moving objects, we add the improved lightweight object detection network YOLOv4-tiny to detect dynamic regions, and the dynamic features in the dynamic area are then eliminated in the algorithm tracking stage. In the case of indoor weak texture scenes, while extracting point features the system extracts surface features at the same time. The framework fuses point and surface features to track camera pose. Experiments on the public TUM RGB-D data sets show that compared with the ORB-SLAM3 algorithm in highly dynamic scenes, the root mean square error (RMSE) of the absolute path error of the proposed algorithm improved by an average of 94.08%. Camera pose is tracked without loss over time. The algorithm takes an average of 34 ms to track each frame of the picture just with a CPU, which is suitably real-time and practical. The proposed algorithm is compared with other similar algorithms, and it exhibits excellent real-time performance and accuracy. We also used a Kinect camera to evaluate our algorithm in complex indoor environment, and also showed high robustness and real-time. To sum up, our algorithm can not only deal with the interference caused by dynamic objects to the system but also stably run in the open indoor weak texture scene.

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Section: Experiments and Results Analysismentioning

confidence: 99%

YOLOv4‐tiny‐based robust RGB‐D SLAM approach with point and surface feature fusion in complex indoor environments

Chang

2022

Journal of Field Robotics

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“…While the most popular approach remains sensor fusion [53,54], other purely visual approaches have also been proposed, such as, [55] which introduced a dense RGB-D SLAM solution that utilized optical flow residuals to achieve accurate and efficient dynamic/ static segmentation for camera tracking and background reconstruction. Zhang et al [56] took a more direct approach which employed deep learning based human detection, and used graph-based segmentation to separate moving humans from the static environment. They further presented a SLAM benchmark dedicated to dynamic environment SLAM solutions [57].…”

Section: Localization Mapping and Slammentioning

confidence: 99%

Perception for Humanoid Robots

Roychoudhury,

Khorshidi,

Agrawal

et al. 2023

Curr Robot Rep

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Purpose of Review The field of humanoid robotics, perception plays a fundamental role in enabling robots to interact seamlessly with humans and their surroundings, leading to improved safety, efficiency, and user experience. This scientific study investigates various perception modalities and techniques employed in humanoid robots, including visual, auditory, and tactile sensing by exploring recent state-of-the-art approaches for perceiving and understanding the internal state, the environment, objects, and human activities. Recent Findings Internal state estimation makes extensive use of Bayesian filtering methods and optimization techniques based on maximum a-posteriori formulation by utilizing proprioceptive sensing. In the area of external environment understanding, with an emphasis on robustness and adaptability to dynamic, unforeseen environmental changes, the new slew of research discussed in this study have focused largely on multi-sensor fusion and machine learning in contrast to the use of hand-crafted, rule-based systems. Human robot interaction methods have established the importance of contextual information representation and memory for understanding human intentions. Summary This review summarizes the recent developments and trends in the field of perception in humanoid robots. Three main areas of application are identified, namely, internal state estimation, external environment estimation, and human robot interaction. The applications of diverse sensor modalities in each of these areas are considered and recent significant works are discussed.

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“…Their method is able to construct a semi-dense map and handle slightly dynamic environments. ZHANG et al [28] propose a method to handle image blur from robot motion, dynamic scene elements and tracking failures, based on a surfel fusion system [20]. Dynamic scene parts are assumed to come from human motion.…”

Section: B Visual Perception For Humanoidsmentioning

confidence: 99%

LOLA v1.1 – An Upgrade in Hardware and Software Design for Dynamic Multi-Contact Locomotion

Seiwald

Sygulla

et al. 2021

2020 IEEE-RAS 20th International Conference on Humanoid Robots (Humanoids)

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This paper presents recent and ongoing hardware and software upgrades to our humanoid robot LOLA. The purpose of these modifications is to achieve dynamic multicontact locomotion, i. e., fast bipedal walking with additional hand-environment support for increased stability and robustness against unforeseen disturbances. The upper body of LOLA has been completely redesigned with an enhanced lightweight torso frame and more robust arms with additional degrees of freedom, which extend the reachable workspace. The mechanical structure of the torso is optimized for stiffness with the help of an experimental modal analysis performed on the real hardware, while the new arm topology is the result of kinematic optimization for typical use-cases in a multi-contact setting. We also propose extensive changes to our software framework, which include a complete redesign of the onboard, real-time perception and navigation module. Although the hardware upgrade is finished and the overall software design is complete, the implementation of various modules is still work in progress.

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Dense RGB-D SLAM for Humanoid Robots in the Dynamic Humans Environment

Cited by 12 publications

References 17 publications

YOLOv4‐tiny‐based robust RGB‐D SLAM approach with point and surface feature fusion in complex indoor environments

YOLOv4‐tiny‐based robust RGB‐D SLAM approach with point and surface feature fusion in complex indoor environments

Perception for Humanoid Robots

LOLA v1.1 – An Upgrade in Hardware and Software Design for Dynamic Multi-Contact Locomotion

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