{"id":243,"date":"2019-04-06T02:44:53","date_gmt":"2019-04-06T02:44:53","guid":{"rendered":"http:\/\/mrsdprojects.ri.cmu.edu\/2018teame\/?page_id=243"},"modified":"2019-10-09T04:48:11","modified_gmt":"2019-10-09T04:48:11","slug":"navigation-localization-and-planning","status":"publish","type":"page","link":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teame\/system-design\/navigation-localization-and-planning\/","title":{"rendered":"Navigation, Localization and Planning"},"content":{"rendered":"

Progress Review 2<\/h2>\n

3D Mapping using LOAM algorithm (Lidar Odometry and Mapping)\"\"<\/h3>\n

\"\"<\/p>\n

Particle Filter Measurement Model performance analysis\u00a0\"\"<\/h3>\n

We started with a navigation architecture that relied on doing lidar SLAM of the farm to generate a dense pointcloud map, and deriving a sensor model from that using a gaussian mixture model. We found this particle filter unstable and ultimately abandoned it for a simpler approach.<\/p>\n

Progress Review 3 and 4<\/h2>\n

Navigation System Architecture\u00a0\"\"<\/h3>\n

Graphical representation of mapping and planning\u00a0\"\"<\/h3>\n

We planned a simpler navigation system which would rely on a pre-defined “map” of row lines and would use row detections of those row lines to localize within the field.<\/p>\n

Progress Reviews 5 & 6 (SVD & SVD Encore)<\/h2>\n

\"\"<\/p>\n

We converged on a navigation strategy for SVD which relied on fusing visual odometry and row detections with a particle filter. We were able to achieve our SVD goals with this approach.<\/p>\n

Progress Review 7<\/h2>\n

\"\"<\/p>\n

Our spring field testing with real plants at Rivendale was not promising. In order to de-risk, we implemented an RTK based localization system in Fall 2019. This system unfortunately suffered from poor yaw estimation from our magetometer, leading to poor roll correction and ultimately poor localization.<\/p>\n

Progress Review 8<\/h2>\n

\"\"<\/p>\n

With a new weed suppression net installed on the field, and challenges faced with RTK localization, we re-visited lidar row segmentation based localization and concluded our previous lidar segmentation + particle filter approach was plausible.<\/p>\n","protected":false},"excerpt":{"rendered":"

Progress Review 2 3D Mapping using LOAM algorithm (Lidar Odometry and Mapping) Particle Filter Measurement Model performance analysis\u00a0 We started with a navigation architecture that relied on doing lidar SLAM of the farm to generate a dense pointcloud map, and deriving a sensor model from that using a gaussian mixture model. We found this particle … Continue reading “Navigation, Localization and Planning”<\/span><\/a><\/p>\n","protected":false},"author":158,"featured_media":0,"parent":45,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-243","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teame\/wp-json\/wp\/v2\/pages\/243","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teame\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teame\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teame\/wp-json\/wp\/v2\/users\/158"}],"replies":[{"embeddable":true,"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teame\/wp-json\/wp\/v2\/comments?post=243"}],"version-history":[{"count":5,"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teame\/wp-json\/wp\/v2\/pages\/243\/revisions"}],"predecessor-version":[{"id":408,"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teame\/wp-json\/wp\/v2\/pages\/243\/revisions\/408"}],"up":[{"embeddable":true,"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teame\/wp-json\/wp\/v2\/pages\/45"}],"wp:attachment":[{"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teame\/wp-json\/wp\/v2\/media?parent=243"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}