Object Detection and Tracking for Autonomous Navigation in Dynamic Environments

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A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “Kalman filtering is sufficient for simple linear models, while the extended and and unscented Kalman filters [186] are used to handle nonlinear dynamic models [187].”
Distributed multi-robot collision avoidance via deep reinforcement learning for navigation in complex scenarios (2020, background): “There are many previous works about navigating a robot toward its goals in a dense human crowd without any collisions (Ess et al., 2010).”
A category-level 3-D object dataset: Putting the Kinect to work (2011, background): “Other datasets: Beyond these, other datasets have been made available which do include simultaneous capture of image and depth but serve more specialized purposes like autonomous driving [1], pedestrian detection [9] and driver assistance [24].”
Self-Driving Cars: A Survey (2019, methodology): “[ESS10] propose a method for obstacle detection and recognition that uses only synchronized video from a forward-”