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We present Human and Geometric Importance SLAM (HGI-SLAM), a novel approachto loop closure using salient and geometric features. Loop closure is a keyelement of SLAM, with many established methods for this problem. However,current methods are narrow, using either geometric or salient based features.We merge their successes into a model that outperforms both types of methodsalone. Our method utilizes inexpensive monocular cameras and does not depend ondepth sensors nor Lidar. HGI-SLAM utilizes geometric and salient features,processes them into descriptors, and optimizes them for a bag of wordsalgorithm. By using a concurrent thread and combing our loop closure detectionwith ORB-SLAM2, our system is a complete SLAM framework. We present extensiveevaluations of HGI loop detection and HGI-SLAM on the KITTI and EuRoC datasets.We also provide a qualitative analysis of our features. Our method runs in realtime, and is robust to large viewpoint changes while staying accurate inorganic environments. HGI-SLAM is an end-to-end SLAM system that only requiresmonocular vision and is comparable in performance to state-of-the-art SLAMmethods.

HGI-SLAM: Loop Closure With Human and Geometric Importance Features