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 proprioceptive velocity factor in the EKF prediction step.  To cope with foot slippage and aggressive motions, we further design a dynamic weighting scheme that adaptively adjusts the confidence of the joint-based velocity factor according to the current motion speed,  while treating IMU measurements as filter observations rather than inputs. The resulting system maintains the localization accuracy of state-of-the-art LIO methods and significantly suppresses high-frequency jitter in the estimated trajectory. Extensive experiments on a real quadruped robot in challenging outdoor scenarios, including steep staircases and large-scale loop trajectories, show that LIJO produces smoother and more stable odometry than existing LIO and kinematic-inertial baselines, while preserving real-time performance. The proposed approach thus provides more reliable state inputs for perception and control modules in all-terrain legged locomotion.