CILC：用于多智能体协作SLAM的加密安全的智能体间闭环候选检测
CILC: Cryptographically-secure Inter-agent Loop Closure Candidate Detection for Multi-Agent Collaborative SLAM
AI总结：
研究多智能体协作SLAM中智能体间闭环候选检测问题，提出CILC系统，利用安全多方计算仅对隐私敏感且计算量轻的GD相似性比较步骤进行加密处理，在模拟和硬件实验中验证其能保持实时性、通信可行并减轻信息泄露。
摘要：
多智能体同时定位与地图构建（SLAM）和协作SLAM（CSLAM）要求机器人持续交换全局描述符（GDs）以检测智能体间闭环（ILCs）。虽然加密无线电可保护通信免受外部窃听者攻击，但无法防范受感染的群体成员。我们通过展示受损智能体如何从其公共GD广播中重建诚实智能体的图像和轨迹近似值，证明了这种威胁的存在。为解决此问题，我们提出了CILC（加密安全的智能体间闭环候选检测），这是首个利用安全多方计算（SMPC）在不清晰交换GDs的情况下检测ILC候选的系统。我们仅将SMPC应用于ILC候选检测（即GD相似性比较）这一隐私敏感但计算量轻的步骤，而非保护整个CSLAM管道，实现了有利的隐私与开销权衡。我们在模拟和硬件实验中验证了CILC在多模态GDs（视觉和激光雷达）上保持实时性和通信可行性，同时减轻了向受损群体智能体的信息泄露。
英文摘要：
Multi-agent Simultaneous Localization and Mapping (SLAM) and collaborative SLAM (CSLAM) require robots to continuously exchange global descriptors (GDs) to detect inter-agent loop closures (ILCs). While encrypted radios protect this traffic from external eavesdroppers, they offer no protection against a compromised swarm member. We show this threat is concrete by demonstrating how a corrupted agent can reconstruct approximations of an honest agent's imagery and trajectory from its public GD broadcasts. To address this, we propose CILC (Cryptographically-secure Inter-agent Loop Closure candidate detection), a first-of-its-kind system leveraging Secure Multi-Party Computation (SMPC) to detect ILC candidates without exchanging GDs in the clear. Rather than securing the entire CSLAM pipeline, we apply SMPC only to ILC candidate detection (i.e., GD similarity comparison), a privacy-sensitive yet computationally lightweight step, yielding an advantageous privacy-to-overhead trade-off. We validate in both simulation and hardware experiments that CILC remains real-time and communication-feasible across multimodal GDs (visual and LiDAR), while mitigating information leakage to a compromised swarm agent.
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