Visual-Inertial RGB-D SLAM With Encoders for a Differential Wheeled Robot

Zhu, Zhanghao; Kaizu, Yutaka; Furuhashi, Kenichi; Imou, Kenji

doi:10.1109/jsen.2021.3101370

Cited by 11 publications

(5 citation statements)

References 19 publications

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“…4-Fig. 6, our proposed method has better accuracy than ORBSLAM3 and VEOS2 proposed by method 18 . This is because we optimize the camera trajectory using co-constrained constraints from different sensor data, and we also use planar constraints, but the average running time is slightly higher than the other two methods, due to the fact that our proposed method also needs to process encoder data, but our proposed method can still meet the realtime requirements.…”

Section: Analyzementioning

confidence: 77%

“…We use the rosbag tool in the ROS robot operating system to load the dataset. Table 1 and Table 2 show the experimental results of our proposed method at different frequencies, and compare with the methods proposed by method 11,17,18 . Global BA is used after SLAM is executed.…”

Section: Experimental Data and Resultsmentioning

confidence: 99%

“…To evaluate our proposed method, we use the dataset produced by method 18 , which is divided into indoor labs, indoor corridors, and small outdoor farms. We use the rosbag tool in the ROS robot operating system to load the dataset.…”

Section: Experimental Data and Resultsmentioning

confidence: 99%

“…Instead, we use the wheel encoder data to jointly estimate the absolute scale. The method proposed in method 18 uses the encoder to constrain the camera trajectory with the worst performance, which is caused by the fact that the wheel encoder is prone to sliding friction when turning, and the sensor data collection error is large.…”

Section: Analyzementioning

confidence: 99%

See 3 more Smart Citations

Visual-inertial odometry based on tightly-coupled encoder

Guo

Luo

et al. 2023

Optoelectronic Imaging and Multimedia Technology IX

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In order to solve the problem of inaccurate scale optimization of visual inertial odometer (VIO) algorithm under uniform motion, this paper presents a Visual-Inertial-Encoder Tightly-Coupled Odometry (VIETO) algorithm, and describes VIETO initialization as an optimal estimation problem in the sense of maximum-a-posteriori (MAP) estimation. Firstly, the pre-integration theory of encoder is introduced in this paper so that the scale and velocity information can be obtained by using the encoder to measure the pre-integration during the visual MAP estimation, which provides a good initial value for the optimal estimation of IMU parameters. Secondly, the encoder error term and random plane constraint are introduced into the visual inertia optimization framework to further constrain pose estimation. Finally, we apply VIETO to the monocular inertial ORB-SLAM3 system. By comparing the algorithm with other similar algorithms on the DS dataset, the results prove the effectiveness of the system.

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Section: Analyzementioning

confidence: 77%

Section: Experimental Data and Resultsmentioning

confidence: 99%

Section: Experimental Data and Resultsmentioning

confidence: 99%

Section: Analyzementioning

confidence: 99%

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Visual-inertial odometry based on tightly-coupled encoder

Guo

Luo

et al. 2023

Optoelectronic Imaging and Multimedia Technology IX

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“…Robotic grasping has made great progress in recent years [5]. The development of low-cost red-green-blue and depth (RGB-D) sensors has led to more and more applications of depth information in robotics [6], [7], [8]; however, since most advanced grasp detection algorithms heavily rely on depth information from these RGB-D sensors, they cannot be applied to transparent and reflective objects since these RGB-D sensors cannot construct a complete depth image for transparent and reflective objects.…”

Section: Introductionmentioning

confidence: 99%

SG-Grasp: Semantic Segmentation Guided Robotic Grasp Oriented to Weakly Textured Objects Based on Visual Perception Sensors

Tong,

Song,

Tian

et al. 2023

IEEE Sensors J.

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Weakly textured objects are frequently manipulated by industrial and domestic robots, and the most common two types are transparent and reflective objects; however, their unique visual properties present challenges even for advanced grasp detection algorithms. Many existing algorithms heavily rely on depth information, which is not accurately provided by ordinary red-green-blue and depth (RGB-D) sensors for transparent and reflective objects. To overcome this limitation, we propose an innovative solution that uses semantic segmentation to effectively segment weakly textured objects and guide grasp detection. By using only red-green-blue (RGB) images from RGB-D sensors, our segmentation algorithm (RTSegNet) achieves state-of-the-art performance on the newly proposed TROSD dataset. Importantly, our method enables robots to grasp transparent and reflective objects without requiring retraining of the grasp detection network (which is trained solely on the Cornell dataset). Real-world robot experiments demonstrate the robustness of our approach in grasping commonly encountered weakly textured objects; furthermore, results obtained from various datasets validate the effectiveness and robustness of our segmentation algorithm. Code and video are available at: https://github.com/meiguiz/SG-Grasp.

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Visual localization on point and line combination in dynamic environments

Wei,

Wang

et al. 2024

Engineering Applications of Artificial Intelligence

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Visual-Inertial RGB-D SLAM With Encoders for a Differential Wheeled Robot

Cited by 11 publications

References 19 publications

Visual-inertial odometry based on tightly-coupled encoder

Visual-inertial odometry based on tightly-coupled encoder

SG-Grasp: Semantic Segmentation Guided Robotic Grasp Oriented to Weakly Textured Objects Based on Visual Perception Sensors

Visual localization on point and line combination in dynamic environments

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