MirrorDrift: 基于运动镜的LiDAR SLAM攻击
MirrorDrift: Actuated Mirror-Based Attacks on LiDAR SLAM
AI总结：
MirrorDrift利用可动平面镜产生鬼点，通过镜面反射攻击LiDAR SLAM，导致定位误差显著增加。
摘要：
LiDAR SLAM提供高精度定位，但容易受到点云损坏的影响，因为扫描匹配假设几何一致性。先前针对LiDAR SLAM的物理攻击大多依赖于外部信号注入的LiDAR欺骗，这需要传感器特定的定时知识，并且越来越多地被现代防御机制如定时混淆和注入拒绝所缓解。在本工作中，我们表明镜面反射提供了一种无需注入的替代方案，并展示了一种攻击，MirrorDrift，它使用一个可动平面镜在LiDAR扫描中产生鬼点，并系统地偏转扫描匹配对应关系。MirrorDrift优化了镜子的位置、对齐和驱动。在模拟中，它将平均姿态误差（APE）比随机放置提高了6.1倍，使三个SLAM系统降级到2.29-3.31米的平均APE。在现实世界实验中，针对现代LiDAR和最先进的干扰抑制，它诱导了高达6.03米的定位误差。据我们所知，这是首次针对生产级安全LiDAR的SLAM针对性攻击。
英文摘要：
LiDAR SLAM provides high-accuracy localization but is fragile to point-cloud corruption because scan matching assumes geometric consistency. Prior physical attacks on LiDAR SLAM largely rely on LiDAR spoofing via external signal injection, which requires sensor-specific timing knowledge and is increasingly mitigated by modern defense mechanisms such as timing obfuscation and injection rejection. In this work, we show that specular reflection offers an injection-free alternative and demonstrate an attack, MirrorDrift, that uses an actuated planar mirror to cause ghost points in LiDAR scans and systematically bias scan-matching correspondences. MirrorDrift optimizes mirror placement, alignment, and actuation. In simulation, it increases the average pose error (APE) by 6.1x over random placement, degrading three SLAM systems to 2.29-3.31 m mean APE. In real-world experiments on a modern LiDAR with state-of-the-art interference mitigation, it induces localization errors of up to 6.03 m. To the best of our knowledge, this is the first successful SLAM-targeted attack against production-grade secure LiDARs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
