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About this paper
Qingrui Zhao, Mingyuan Li, Yongliang Shi, Xuechao Chen, Zhangguo Yu, Lianqiang Han, Zhenyuan Fu, Jintao Zhang and 3 more
LIKO combines IMU, LiDAR, and leg odometry in an iterated Kalman filter for biped state estimation. It uses planted-foot position and leg motion for timely velocity updates, while LiDAR corrects global position drift. On BHR-B3’s square_walk sequence, it reports 0.0198 m APE RMSE and 0.0018% translational RPE RMSE, versus FastLIO2’s 0.0294 m and 0.0059%; this result is sequence-specific.
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