SUMMARY
Simultaneous Localisation And Mapping (SLAM) In Dynamic Environments (IDE) may be improved by detecting and removing moving objects that may otherwise lead to localisation errors. This work combines convolutional neural networks and feature clustering to serve as A Moving Object detection and REmoval method (AMORE) that removes moving objects from the SLAM process and improves the performance of SLAMIDE. Experiments show that a visual SLAM algorithm and AMORE combined are more robust with high-dynamic objects than the SLAM algorithm alone, and performance is comparable to state-of-the-art visual SLAMIDE approaches. AMORE has the advantage of simplicity, requiring minimal implementation effort.