ClusterSLAM: A SLAM backend for simultaneous rigid body clustering and motion estimation

Huang, Jiahui; Yang, Songfan; Zhao, Zishuo; Lai, Yu‐Kun; Hu, Shi‐Min

doi:10.1007/s41095-020-0195-3

Cited by 29 publications

(42 citation statements)

References 49 publications

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“…After solving linear equation ( 12), the external parameter matrix from the RGBD camera to the laser range finder can be calculated. en, we substituteΦandΔinto equation (8) and transfer each laser scanning point. e calibration between the s scanning point and the point cloud can be calibrated.…”

Section: Registration Of Point Cloud To the Laser Range Findermentioning

confidence: 99%

“…For example, some inevitable changes, lights, people moving, and object moving can cause the loss of position and posture of mobile robots [5] and, in turn, lead to inaccurate mapping. In order to deal with some dynamic objects that may exist in large scenes, the current algorithm usually eliminates dynamic objects by adding object detection or image segmentation algorithm based on deep learning in the system, such as Dynamic SLAM [6], Cluster VO [7], and Cluster SLAM [8] algorithm [9]. However, this will inevitably lead to a large consumption of computing resources, and some microdynamic objects are usually distributed near the camera.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A 3D Reconstruction Method Using Multisensor Fusion in Large-Scale Indoor Scenes

Zhou

Yu³

et al. 2020

Complexity

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RGBD camera-based VSLAM (Visual Simultaneous Localization and Mapping) algorithm is usually applied to assist robots with real-time mapping. However, due to the limited measuring principle, accuracy, and distance of the equipped camera, this algorithm has typical disadvantages in the large and dynamic scenes with complex lightings, such as poor mapping accuracy, easy loss of robot position, and much cost on computing resources. Regarding these issues, this paper proposes a new method of 3D interior construction, which combines laser radar and an RGBD camera. Meanwhile, it is developed based on the Cartographer laser SLAM algorithm. The proposed method mainly takes two steps. The first step is to do the 3D reconstruction using the Cartographer algorithm and RGBD camera. It firstly applies the Cartographer algorithm to calculate the pose of the RGBD camera and to generate a submap. Then, a real-time 3D point cloud generated by using the RGBD camera is inserted into the submap, and the real-time interior construction is finished. The second step is to improve Cartographer loop-closure quality by the visual loop-closure for the sake of correcting the generated map. Compared with traditional methods in large-scale indoor scenes, the proposed algorithm in this paper shows higher precision, faster speed, and stronger robustness in such contexts, especially with complex light and dynamic objects, respectively.

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Section: Registration Of Point Cloud To the Laser Range Findermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A 3D Reconstruction Method Using Multisensor Fusion in Large-Scale Indoor Scenes

Zhou

Yu³

et al. 2020

Complexity

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“…The MODT integrated SLAM paradigm can broadly be classified into the categories of Loosely Coupled and Tightly Coupled approaches [ 25 ]. The loosely coupled approaches perform MODT and SLAM separately, whereas tightly coupled approaches operate in an integrated fashion [ 19 , 20 , 21 , 22 , 23 , 24 , 25 ].…”

Section: Related Workmentioning

confidence: 99%

“…The loosely coupled approaches [ 8 , 15 , 16 , 17 , 18 ] perform environmental perception and SLAM separately. On the other hand, tightly coupled approaches solve both problems in an integrated fashion [ 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. Both approaches have their pros and cons, but the core issue boils down to the computational resource requirements of perception and real-time capability of the entire framework, which is largely left unattended.…”

Section: Introductionmentioning

confidence: 99%

Semantics Aware Dynamic SLAM Based on 3D MODT

Sualeh

Kim

2021

Sensors

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The idea of SLAM (Simultaneous Localization and Mapping) being a solved problem revolves around the static world assumption, even though autonomous systems are gaining environmental perception capabilities by exploiting the advances in computer vision and data-driven approaches. The computational demands and time complexities remain the main impediment in the effective fusion of the paradigms. In this paper, a framework to solve the dynamic SLAM problem is proposed. The dynamic regions of the scene are handled by making use of Visual-LiDAR based MODT (Multiple Object Detection and Tracking). Furthermore, minimal computational demands and real-time performance are ensured. The framework is tested on the KITTI Datasets and evaluated against the publicly available evaluation tools for a fair comparison with state-of-the-art SLAM algorithms. The results suggest that the proposed dynamic SLAM framework can perform in real-time with budgeted computational resources. In addition, the fused MODT provides rich semantic information that can be readily integrated into SLAM.

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“…While research on reconstructing and modeling static indoor scenes [8] has matured in the past few years, reconstructing dynamic objects (e.g., humans, animals, and other freely moving objects) still remains an open problem in both the graphics and robotics communities (referred to as dynamic SLAM [9][10][11][12][13]). Given an input sequence recording a non-rigid deforming object, the goal of dynamic reconstruction is to recover the moving object's underlying shape in a canonical pose as well as the deformation field for each frame so that the geometry at each instant of time can be recovered.…”

Section: Introductionmentioning

confidence: 99%

HDR-Net-Fusion: Real-time 3D dynamic scene reconstruction with a hierarchical deep reinforcement network

et al. 2021

Self Cite

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Reconstructing dynamic scenes with commodity depth cameras has many applications in computer graphics, computer vision, and robotics. However, due to the presence of noise and erroneous observations from data capturing devices and the inherently ill-posed nature of non-rigid registration with insufficient information, traditional approaches often produce low-quality geometry with holes, bumps, and misalignments. We propose a novel 3D dynamic reconstruction system, named HDR-Net-Fusion, which learns to simultaneously reconstruct and refine the geometry on the fly with a sparse embedded deformation graph of surfels, using a hierarchical deep reinforcement (HDR) network. The latter comprises two parts: a global HDR-Net which rapidly detects local regions with large geometric errors, and a local HDR-Net serving as a local patch refinement operator to promptly complete and enhance such regions. Training the global HDR-Net is formulated as a novel reinforcement learning problem to implicitly learn the region selection strategy with the goal of improving the overall reconstruction quality. The applicability and efficiency of our approach are demonstrated using a large-scale dynamic reconstruction dataset. Our method can reconstruct geometry with higher quality than traditional methods.

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ClusterSLAM: A SLAM backend for simultaneous rigid body clustering and motion estimation

Cited by 29 publications

References 49 publications

A 3D Reconstruction Method Using Multisensor Fusion in Large-Scale Indoor Scenes

A 3D Reconstruction Method Using Multisensor Fusion in Large-Scale Indoor Scenes

Semantics Aware Dynamic SLAM Based on 3D MODT

HDR-Net-Fusion: Real-time 3D dynamic scene reconstruction with a hierarchical deep reinforcement network

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