{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fast-trajectory-simplification-algorithm-for","title":"Fast Trajectory Simplification Algorithm for Natural User Interfaces in Robot Programming by Demonstration","arxiv_id":"1608.07338","date":"2016-08-25","proceeding":null,"authors":["Daniel L. Marino","Milos Manic"],"abstract":"Trajectory simplification is a problem encountered in areas like Robot\nprogramming by demonstration, CAD/CAM, computer vision, and in GPS-based\napplications like traffic analysis. This problem entails reduction of the\npoints in a given trajectory while keeping the relevant points which preserve\nimportant information. The benefits include storage reduction, computational\nexpense, while making data more manageable. Common techniques formulate a\nminimization problem to be solved, where the solution is found iteratively\nunder some error metric, which causes the algorithms to work in super-linear\ntime. We present an algorithm called FastSTray, which selects the relevant\npoints in the trajectory in linear time by following an open loop heuristic\napproach. While most current trajectory simplification algorithms are tailored\nfor GPS trajectories, our approach focuses on smooth trajectories for robot\nprogramming by demonstration recorded using motion capture systems.Two\nvariations of the algorithm are presented: 1. aims to preserve shape and\ntemporal information; 2. preserves only shape information. Using the points in\nthe simplified trajectory we use cubic splines to interpolate between these\npoints and recreate the original trajectory. The presented algorithm was tested\non trajectories recorded from a hand-tracking system. It was able to eliminate\nabout 90% of the points in the original trajectories while maintaining errors\nbetween 0.78-2cm which corresponds to 1%-2.4% relative error with respect to\nthe bounding box of the trajectories.","url_abs":"http://arxiv.org/abs/1608.07338v1","url_pdf":"http://arxiv.org/pdf/1608.07338v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fast-trajectory-simplification-algorithm-for","repo_url":"https://github.com/hadjigagny93/FastStray","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}