Optics and Precision Engineering, Volume. 32, Issue 6, 857(2024)
RGB-D SLAM method of dynamic scene based on instance segmentation and optical flow
[1] [1] 张裕, 张越, 张宁, 等. 基于逆深度滤波的双目折反射全景相机动态SLAM系统[J]. 光学 精密工程, 2022, 30(11): 1282-1289. doi: 10.37188/ope.20223011.1282ZHANGY, ZHANGY, ZHANGN, et al. Dynamic SLAM of binocular catadioptric panoramic camera based on inverse depth filter[J]. Opt. Precision Eng., 2022, 30(11): 1282-1289.(in Chinese). doi: 10.37188/ope.20223011.1282
[2] [2] 郭道亮. 可变形物体的全局非刚性配准与重建[D]. 天津: 天津大学, 2018.GUOD L. Global Non-Rigid Registration and Reconstruction of Deformable Objects[D]. Tianjin: Tianjin University, 2018. (in Chinese)
[3] R A NEWCOMBE, S M SEITZ. DynamicFusion: reconstruction and tracking of non-rigid scenes in real-time, 343-352(2015).
[4] [4] 刘东生, 陈建林, 费点, 等. 基于深度相机的大场景三维重建[J]. 光学 精密工程, 2020, 28(1): 234-243. doi: 10.3788/ope.20202801.0234LIUD S, CHENJ L, FEID, et al. Three-dimensional reconstruction of large-scale scene based on depth camera[J]. Opt. Precision Eng., 2020, 28(1): 234-243.(in Chinese). doi: 10.3788/ope.20202801.0234
[5] R MUR-ARTAL, J D TARDÓS. ORB-SLAM2: an open-source SLAM system for monocular, stereo, and RGB-D cameras. IEEE Transactions on Robotics, 33, 1255-1262(2017).
[6] R MUR-ARTAL, J M M MONTIEL, J D TARDÓS. ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Transactions on Robotics, 31, 1147-1163(2015).
[7] C CAMPOS, R ELVIRA, J J G RODRÍGUEZ et al. ORB-SLAM3: an accurate open-source library for visual, visual-inertial, and multimap SLAM. IEEE Transactions on Robotics, 37, 1874-1890(2021).
[8] XS ANDUAGA, S ANTONELLI et al. The ATLAS experiment at the CERN large hadron collider(2008).
[9] R GIRSHICK, J DONAHUE, T DARRELL et al. Rich feature hierarchies for accurate object detection and semantic segmentation, 580-587(2014).
[10] K M HE, G GKIOXARI, P DOLLÁR et al. Mask R-CNN, 2980-2988(2017).
[11] S Q REN, K M HE, R GIRSHICK et al. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).
[13] X L WANG, T KONG, C H SHEN et al.
[14] D Q SUN, X D YANG, M Y LIU et al. PWC-net: CNNs for optical flow using pyramid, warping, and cost volume, 8934-8943(2018).
[15] Z TEED, J DENG.
[17] S IZADI, D KIM, O HILLIGES et al. KinectFusion: Real-Time 3D reconstruction and interaction using a moving depth camera, 559-568(2011).
[18] R A NEWCOMBE, S IZADI, O HILLIGES et al. KinectFusion: Real-Time dense surface mapping and tracking, 127-136(2011).
[19] M NIEßNER, M ZOLLHÖFER, S IZADI et al. Real-time 3D reconstruction at scale using voxel hashing. ACM Transactions on Graphics, 32, 1-11(2013).
[20] M RUNZ, M BUFFIER, L AGAPITO. MaskFusion: Real-Time recognition, tracking and reconstruction of multiple moving objects, 10-20(2018).
[21] T WHELAN, S LEUTENEGGER, R SALAS MORENO et al. ElasticFusion: dense SLAM without a pose graph, 11(2015).
[22] B BESCOS, J M FÁCIL, J CIVERA et al. DynaSLAM: tracking, mapping, and inpainting in dynamic scenes. IEEE Robotics and Automation Letters, 3, 4076-4083(2018).
[23] B B XU, W B LI, D TZOUMANIKAS et al. MID-Fusion: octree-based object-level multi-instance dynamic SLAM, 5231-5237(2019).
[24] S RUSINKIEWICZ, M LEVOY. Efficient variants of the ICP algorithm, 145-152(2002).
[25] W X WU, L GUO, H L GAO et al. YOLO-SLAM: a semantic SLAM system towards dynamic environment with geometric constraint. Neural Computing and Applications, 34, 6011-6026(2022).
[26] D S KIM, K W FIGUEROA, K W LI et al. Profiling of dynamically changed gene expression in dorsal root Ganglia post peripheral nerve injury and a critical role of injury-induced glial fibrillary acidic protein in maintenance of pain behaviors. Pain, 143, 114-122(2009).
[27] P F ALCANTARILLA, J J YEBES, J ALMAZÁN et al. On combining visual SLAM and dense scene flow to increase the robustness of localization and mapping in dynamic environments, 1290-1297(2012).
[28] T W ZHANG, H Y ZHANG, Y LI et al. FlowFusion: dynamic dense RGB-D SLAM based on optical flow, 7322-7328(2020).
[29] W B LIN, C W ZHENG, J H YONG et al. OcclusionFusion: occlusion-aware motion estimation for real-time dynamic 3D reconstruction, 1726-1735(2022).
[30] F SCARSELLI, M GORI, A C TSOI et al. The graph neural network model. IEEE Transactions on Neural Networks, 20, 61-80(2009).
[31] M BUJANCA, B LENNOX, M LUJÁN. ACEFusion-accelerated and energy-efficient semantic 3D reconstruction of dynamic scenes, 11063-11070(2022).
[32] J STURM, N ENGELHARD, F ENDRES et al. A benchmark for the evaluation of RGB-D SLAM Systems, 573-580(2012).
[33] E PALAZZOLO, J BEHLEY, P LOTTES et al. ReFusion: 3D Reconstruction in dynamic environments for RGB-D cameras exploiting residuals, 7855-7862(2019).
[34] W E LORENSEN, H E CLINE. Marching cubes: a high resolution 3D surface construction algorithm. ACM SIGGRAPH Computer Graphics, 21, 163-169(1987).
[36] R SCONA, M JAIMEZ, Y R PETILLOT et al. StaticFusion: background reconstruction for dense RGB-D SLAM in dynamic environments, 3849-3856(2018).
[37] Y S WONG, C J LI, M NIEßNER et al. RigidFusion: RGB-D scene reconstruction with rigidly-moving objects. Computer Graphics Forum, 40, 511-522(2021).
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Chenggen WANG, Jinlong SHI, Haowei ZHU, Suqin BAI, Yunhan SUN, Jiawen LU, Shucheng HUANG. RGB-D SLAM method of dynamic scene based on instance segmentation and optical flow[J]. Optics and Precision Engineering, 2024, 32(6): 857
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Received: Sep. 7, 2023
Accepted: --
Published Online: Apr. 19, 2024
The Author Email: SHI Jinlong (shi_jinlong@163. com)