Smooth LiDAR–Inertial–Joint Odometry for perception-driven legged locomotion
: Light Detection and Ranging (LiDAR)–Inertial Odometry (LIO), which tightly fuses complementary data from LiDAR and Inertial Measurement Units (IMUs), is a key technology for high-precision state estimation in legged robot navigation. However, conventional Iterative Closest Point (ICP)-based LIO frameworks provide only pose constraints. Their position estimates often exhibit centimetre-level jitter due to LiDAR measurement noise, especially when the robot is stationary or moving slowly. This temporal inconsistency degrades the performance of downstream perception-driven motion planning and control. In this paper, we propose LiDAR–Inertial–Joint Odometry (LIJO), a novel state estimation framework for quadruped robots that integrates LiDAR, IMU, and joint encoder measurements within a manifold extended Kalman filter (EKF). The torso velocity is first estimated from joint angles and angular velocities via forward kinematics and is then used as a