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.

Due to the space-time continuity of the physical attributes, such as temperature and illumination, high spatio-temporal correlation exists among the sensed data in the high-density Wireless Sensor Network (WSN). The data redundancy produced by the correlation brings heavy burden to network communication and shortens the networks lifetime. A Clustered Data Collection Framework (CDCF) based on prediction model was proposed to explore the data correlation and reduce the network traffic. The framework included a time series prediction model based on curve fitting least square method and an efficient error control strategy. In the process of data collection, the clustered structure considered the spatial correlation, and the time series prediction model investigated the temporal correlation existing in sensed data. The experimental simulation proves that CDCF used only 10%—20% of the amount of raw data to finish the data collection of the networks in the relatively stable environment, and the error of the data restored in sink is less than the threshold value which defined by user.

The suitability analysis of matching area plays a significant role in the field of vision-based navigation. There are many feature indexes that can only unilaterally describe the suitability of matching area. An algorithm was proposed to integrate several feature indexes to solve conflicts among different feature indexes and provide a kind of method that can measure the suitable confidence and unsuitable confidence of a feature.And then the confidences were fused by using the Dempster-Shafer (DS) rules. At last the algorithm was verified by simulation experiment.