Robust monocular SLAM in dynamic environments

By Wei Tan, Haomin Liu, Zilong Dong, Guofeng Zhang, Hujun Bao. International Symposium on Mixed and Augmented Reality, 2013

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Visual SLAM and Structure from Motion in Dynamic Environments (2018, background): “[152] also use a similar projection principle to detect dynamic features.”
DynaSLAM: Tracking, Mapping, and Inpainting in Dynamic Scenes (2018, methodology): “t functions (e.g., in PTAM [2]). There are several SLAM systems that address more speciﬁcally the dynamic scene content. Within feature-based SLAM methods, some of the most relevant are: Tan et al. [9] detect changes that take place in the scene by projecting the map features into the current frame”
PL-SLAM: Real-time monocular visual SLAM with points and lines (2017, methodology): “Previous solutions were based on homography [8] or essential matrix estimation [29], and required point correspondences.”
Revisiting Trends in Augmented Reality Research: A Review of the 2nd Decade of ISMAR (2008–2017) (2018, result): “[137] presented a SLAM system that could detect changed features and adaptively update the map to achieve robust tracking and mapping in dynamic environments, and compared its performance to PTAM.”
ORB-SLAM: A Versatile and Accurate Monocular SLAM System (2015, methodology): “[24], where keyframes were discarded after a process of change detection.”