Multisource Fusion-Based Mapping Building and Control Method for Reconnaissance Robots

Scouting and mapping in electromagnetic interference (EMI) environments is critical for reconnaissance robot navigation. A map building and localization solution is proposed that combines factor graph optimization with an optimized loop improvement. Based on the Fast-LIO2 algorithm, the factor graphs are utilized to replace the traditional filtering method to achieve the optimal pose estimation from global observation. Simultaneously, SPFH (shot-point feature histogram) feature points are adopted for loop closure detection of the position and pose of the robots. The experimental results demonstrate the map building effects are improved in both indoor and outdoor environments. The localization accuracy and combat efficiency of reconnaissance robots in complex EMI scenarios are effectively enhanced by the improved optimization and localization method.