基于战场元宇宙的动态三维场景感知

doi:10.16182/j.issn1004731x.joss.23-FZ0815

摘要/Abstract

摘要：

信息化作战对战场的态势感知提出了更高的要求，利用无人智能体进行战场侦察并感知目标信息尤为重要。基于战场元宇宙目标数据与作战环境，面向复杂动态环境定位与目标识别的需求，提出并构建了动态三维场景感知系统，使用视觉和IMU融合传感器仿真数据作为输入，通过实例分割和稠密光流估计网络提取战场目标信息并作为场景先验，在SLAM和优化过程中同步完成无人智能体在战场中的位姿估计与对战场目标的跟踪。实验结果表明：系统能够在战场动态复杂环境下给出场景稀疏重建与无人智能体准确的定位，持续输出每一个战场目标类型、位置、速度等信息，表现出良好的精度与鲁棒性，为战场态势融合等其他模块提供技术支撑。

关键词: 态势感知, 动态场景, SLAM, 实例分割, 战场元宇宙

Abstract:

Informatization combat needs higher requirements for battlefield situational awareness, and the use of unmanned intelligences to conduct battlefield reconnaissance and perceive target information is particularly important. Facing the needs of complex dynamic environment localization and target recognition, a dynamic 3D scene perception system is proposed and constructed based on battlefield meta-universe target data and operational environment,which uses vision and IMU fusion sensor simulation data as inputs, extracts battlefield target information through instance segmentation and dense optical flow estimation network and uses it as a scene prior, and synchronizes the position estimation of unmanned intelligences in the battlefield with the tracking of battlefield targets during SLAM and optimization process. The experimental results show that the system is able to provide the sparse reconstruction of the scene and accurate localization of the unmanned intelligent body under the dynamic and complex environment of the battlefield, and continuously outputs the information of each battlefield target such as type, position, and speed, which shows the good accuracy and robustness, and provides technical support for the battlefield situational fusion and other modules.

Key words: situation awareness, dynamic scenes, SLAM, instance segmentation, battlefield metaverse

中图分类号:

TP391.9

王浩宇,龚光红,蔡继红等 . 基于战场元宇宙的动态三维场景感知[J]. 系统仿真学报, 2023, 35(10): 2262-2278.

Wang Haoyu,Gong Guanghong,Cai Jihong,et al . Dynamic 3D Scene Perception Based on Battlefield Metaverse[J]. Journal of System Simulation, 2023, 35(10): 2262-2278.

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实例命名	类型	是否伪装
APC_Amb	装甲运输车	否
APC	装甲运输车	是
Tank_White	坦克	否
Tank_Green	坦克	是
DF	导弹发射车	是
Helicopter	直升机	否
UAV	作战无人机	否
Army_Jeep	吉普车	是
MRAP	防雷多功能车	是
MTruck	军用卡车	是

文件夹	内容
Mask	json_to_dataset生成的.png格式label文件
Json	labelme生成的json文件
Labelme	json_to_dataset生成的文件夹
Pic	尺寸标准化之后的原图

参数	参数含义	设定值
RPN_ANCHOR_SCALES	特征层锚框大小	(16, 32, 64, 128, 256)
BACKBONE_STRIDES	特征层下采样倍数	[4, 8, 16, 32, 64]
WEIGHT_DECAY	权值衰减	0.000 1
LEARNING_RATE	学习率	0.000 05
LEARNING_MOMENTUM	学习动量	0.9
EPOCHS	训练轮数	500

相机参数	标定值	相机参数	标定值
相机水平焦距/px	533.333 3	基线距离/mm	120
相机垂直焦距/px	533.333 3	帧率/(帧/s)	10
相机光心坐标	(320, 240)

指标	智能体	目标1	目标2	目标3	目标4
RPE_t/m	0.235 5	0.199 3	0.303 8	0.225 7	0.184 9
跟踪帧	350	87	110	52	23