FRL-SLAM: A Fast, Robust and Lightweight SLAM System for Quadruped Robot Navigation

Zhang, Chi; Yang, Zhong; Fang, Qianhui; Xu, Chao-Jiang; Xu, Hao; Xu, Xiangrong; Zhang, Jianwei

doi:10.1109/robio54168.2021.9739499

Cited by 9 publications

(6 citation statements)

References 21 publications

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“…Finally, we utilized our self-developed drone to perform a 3D geometric–semantic scene-understanding test in the real world (in Section 6.3 ). The evaluation for robot state estimation has already been performed in our previous work [ 32 , 33 , 34 , 35 ], so is not a contribution of this article.…”

Section: Methodsmentioning

confidence: 99%

“…The evaluation for robot state estimation has already been performed in our previous work [32][33][34][35], so is not a contribution of this article. Cityscapes [42] is a large-scale dataset that focuses on scene understanding in an urban street background.…”

Section: Methodsmentioning

confidence: 99%

“…Finally, the UAV pose can be obtained by optimizing the UAV state vector

by employing probability factor graph optimization. For the detailed process of factor graph optimization, please refer to our previous literature [ 32 , 33 , 34 , 35 ].…”

Section: Bat-inspired State Estimationmentioning

confidence: 99%

“…Finally, the UAV pose can be obtained by optimizing the UAV state vector  by employing probability factor graph optimization. For the detailed process of factor graph optimization, please refer to our previous literature [32][33][34][35]. Assuming that the measurement noise conforms to a Gaussian distribution with zero mean, then the solution process of the UAV state vector χ can be expressed as:…”

Section: Bat-inspired State Estimationmentioning

confidence: 99%

See 3 more Smart Citations

Perceiving like a Bat: Hierarchical 3D Geometric–Semantic Scene Understanding Inspired by a Biomimetic Mechanism

Zhang,

Yang,

Xue

et al. 2023

Biomimetics

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Geometric–semantic scene understanding is a spatial intelligence capability that is essential for robots to perceive and navigate the world. However, understanding a natural scene remains challenging for robots because of restricted sensors and time-varying situations. In contrast, humans and animals are able to form a complex neuromorphic concept of the scene they move in. This neuromorphic concept captures geometric and semantic aspects of the scenario and reconstructs the scene at multiple levels of abstraction. This article seeks to reduce the gap between robot and animal perception by proposing an ingenious scene-understanding approach that seamlessly captures geometric and semantic aspects in an unexplored environment. We proposed two types of biologically inspired environment perception methods, i.e., a set of elaborate biomimetic sensors and a brain-inspired parsing algorithm related to scene understanding, that enable robots to perceive their surroundings like bats. Our evaluations show that the proposed scene-understanding system achieves competitive performance in image semantic segmentation and volumetric–semantic scene reconstruction. Moreover, to verify the practicability of our proposed scene-understanding method, we also conducted real-world geometric–semantic scene reconstruction in an indoor environment with our self-developed drone.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Finally, the UAV pose can be obtained by optimizing the UAV state vector

by employing probability factor graph optimization. For the detailed process of factor graph optimization, please refer to our previous literature [ 32 , 33 , 34 , 35 ].…”

Section: Bat-inspired State Estimationmentioning

confidence: 99%

Section: Bat-inspired State Estimationmentioning

confidence: 99%

See 2 more Smart Citations

Perceiving like a Bat: Hierarchical 3D Geometric–Semantic Scene Understanding Inspired by a Biomimetic Mechanism

Zhang,

Yang,

Xue

et al. 2023

Biomimetics

Self Cite

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“…Unfortunately, the pose estimation strategy based on vision-IMU-GNSS sensor fusion will face the following problems: Firstly, the output frequencies from each sensor are different (the frequencies of camera, IMU and GNSS receiver are 30, 200, and 10 Hz, respectively). How to fuse these raw measurements from different sensors will be an intractable problem [13,14] ; Secondly, how can the pose estimator quickly restore to normal state when one of the sensors suddenly fails?…”

Section: Introductionmentioning

confidence: 99%

RRVPE: A Robust and Real-Time Visual-Inertial-GNSS Pose Estimator for Aerial Robot Navigation

Zhang¹,

Yang²,

Xu³

et al. 2023

Wuhan Univ. J. Nat. Sci.

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Self-localization and orientation estimation are the essential capabilities for mobile robot navigation. In this article, a robust and real-time visual-inertial-GNSS(Global Navigation Satellite System) tightly coupled pose estimation (RRVPE) method for aerial robot navigation is presented. The aerial robot carries a front-facing stereo camera for self-localization and an RGB-D camera to generate 3D voxel map. Ulteriorly, a GNSS receiver is used to continuously provide pseudorange, Doppler frequency shift and universal time coordinated (UTC) pulse signals to the pose estimator. The proposed system leverages the Kanade Lucas algorithm to track Shi-Tomasi features in each video frame, and the local factor graph solution process is bounded in a circumscribed container, which can immensely abandon the computational complexity in nonlinear optimization procedure. The proposed robot pose estimator can achieve camera-rate (30 Hz) performance on the aerial robot companion computer. We thoroughly experimented the RRVPE system in both simulated and practical circumstances, and the results demonstrate dramatic advantages over the state-of-the-art robot pose estimators.

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MSOT-Q: Multi-Sensor Fusion SLAM with Object Detection Tracking for Quadruped Robots

Xu,

Li,

Yang

et al. 2024

2024 36th Chinese Control and Decision Conference (CCDC)

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FRL-SLAM: A Fast, Robust and Lightweight SLAM System for Quadruped Robot Navigation

Cited by 9 publications

References 21 publications

Perceiving like a Bat: Hierarchical 3D Geometric–Semantic Scene Understanding Inspired by a Biomimetic Mechanism

Perceiving like a Bat: Hierarchical 3D Geometric–Semantic Scene Understanding Inspired by a Biomimetic Mechanism

RRVPE: A Robust and Real-Time Visual-Inertial-GNSS Pose Estimator for Aerial Robot Navigation

MSOT-Q: Multi-Sensor Fusion SLAM with Object Detection Tracking for Quadruped Robots

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