The localization technology of robots is crucial for the efficient， precise， and safe operation of intelligent robots. However， in actual localization processes， robots often encounter the “kidnapping” problem. In response to this challenge， a Self-Recovery Adaptive Monte Carlo Localization （AMCL） algorithm based on Support Vector Machine （SVM-SRAMCL） was proposed. Firstly， a detection model was constructed to identify the “kidnapping” state of the robot， known as the Kidnapping Detection Model based on SVM （SVM-KDM）. Then， particle characteristic values were calculated from the particle set obtained through the AMCL algorithm and used as inputs for SVM-KDM. Once a “kidnapping” event was detected， an Extended Kalman Filter （EKF） was employed to fuse data from the Inertial Measurement Unit （IMU） and Odometry （Odom） to estimate the robot’s new pose. Finally， the AMCL algorithm was utilized for particle prediction， update， and resampling， ultimately achieving the robot’s relocalization. Compared to the Self-Recovery Monte Carlo Localization （SR-MCL） algorithm， the proposed algorithm reduced 4.1 required updates for post-kidnapping recovery and increased the success rate of relocalization by 3 percentage points. The experimental results validate the higher efficiency and success rate of the proposed algorithm when addressing the “kidnapping” issue in the localization of mobile robots.