基于空地协同的无人驾驶环境感知与建图算法设计开题报告

1. 研究目的与意义

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2. 研究内容和预期目标

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3. 研究的方法与步骤

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4. 参考文献

p style=margin-left:0cm;text-indent:0cm; [1]nbsp;nbsp;nbsp;Chen C, Wang B, Lu C X, et al. A survey on deep learning forlocalization and mapping: Towards the age of spatial machine intelligence[J].arXiv preprint arXiv:2006.12567, 2020./pp style=margin-left:0cm;text-indent:0cm; [2]nbsp;nbsp;nbsp;LIM H, KIM Y, JUNG K, et al. Avoiding degeneracy formonocular visual SLAM with point and line features [C]. 2021 IEEE InternationalConference on Robotics and Automation (ICRA), 2021: 11675-81./pp style=margin-left:0cm;text-indent:0cm; [3]nbsp;nbsp;nbsp;STEENBEEK A, NEX F. CNN-based dense monocular visual SLAM forreal-time UAV exploration in emergency conditions [J]. Drones, 2022, 6(3): 79./pp style=margin-left:0cm;text-indent:0cm; [4]nbsp;nbsp;nbsp;姊佹櫒鏅? 鐢伴噾楣? 瀹嬫槬鏋? 鍩轰簬闆疯揪鍜屾憚鍍忓ご浼犳劅鍣ㄨ瀺鍚堢殑杈呭姪椹鹃┒鐩爣妫€娴嬬畻娉?[J]. 淇℃伅鎶€鏈笌淇℃伅鍖? 2021, (12): 5-9./pp style=margin-left:0cm;text-indent:0cm; [5]nbsp;nbsp;nbsp;YOU Y, LOU Y, LIU Q, et al. Pointwise rotation-invariantnetwork with adaptive sampling and 3d spherical voxel convolution [C]. The AAAIConference on Artificial Intelligence, 2020: 12717-24./pp style=margin-left:0cm;text-indent:0cm; [6]nbsp;nbsp;nbsp;DAVISON A J, REID I D, MOLTON N D, et al. MonoSLAM: Real-timesingle camera SLAM [J]. IEEE transactions on pattern analysis and machineintelligence, 2007, 29(6): 1052-67./pp style=margin-left:0cm;text-indent:0cm; [7]nbsp;nbsp;nbsp;L. Li et al., RINet: Efficient 3D Lidar-Based PlaceRecognition Using Rotation Invariant Neural Network, in IEEE Robotics andAutomation Letters, vol. 7, no. 2, pp. 4321-4328, April 2022, doi:10.1109/LRA.2022.3150499./pp style=margin-left:0cm;text-indent:0cm; [8]nbsp;nbsp;nbsp;RUNZ M, BUFFIER M, AGAPITO L. Maskfusion: Real-timerecognition, tracking and reconstruction of multiple moving objects;proceedings of the 2018 IEEE International Symposium on Mixed and AugmentedReality (ISMAR), F, 2018 [C]. IEEE./pp style=margin-left:0cm;text-indent:0cm; [9]nbsp;nbsp;nbsp;L. He, S. Jiang, X. Liang, N. Wang and S. Song,Diff-Net: Image Feature Difference Based High-Definition Map ChangeDetection for Autonomous Driving, 2022 International Conference onRobotics and Automation (ICRA), Philadelphia, PA, USA, 2022, pp. 2635-2641./p[10]nbsp; nbsp;Tao C, Fu S, Wang C, et al.F-PVNet: Frustum-level 3D Object Detection on Point-Voxel FeatureRepresentation for Autonomous Driving[J]. IEEE Internet of Things Journal,2022.

5. 计划与进度安排

p span2023.01.01/span鈥?lt;span2023.01.31nbsp; /span璇鹃璋冪爺锛屼簡瑙ｇ浉鍏虫妧鏈拰瑕佹眰锛?lt;span/span/pp span2023.02.01/span鈥?lt;span2023.02.28nbsp; /span濉啓寮€棰樻姤鍛婏紝瀹屾垚澶栨枃璧勬枡鐨勭炕璇戯紱span/span/pp span2023.03.01/span鈥?lt;span2023.04.30nbsp; /span瀹為獙鍒嗘瀽锛屾暟鎹暣鐞嗭紝瀹屾垚璁烘枃鍒濈锛?lt;span/span/pp span2023.05.01/span鈥?lt;span2023.05.10nbsp; /span璁烘枃鏌ラ噸銆佽皟鏁村苟瀹屽杽銆佷慨鏀硅鏂囷紱span/span/p2023.05.11鈥?023.06.10nbsp; 鏁寸悊璁捐鏂囨。

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