Paper Info
Title
Layered costmaps for context-sensitive navigation
Abstract
Many navigation systems, including the ubiquitous ROS navigation stack, perform path-planning on a single costmap, in which the majority of information is stored in a single grid. This approach is quite successful at generating collision-free paths of minimal length, but it can struggle in dynamic, people-filled environments when the values in the costmap expand beyond occupied or free space. We have created and implemented a new method called layered costmaps, which work by separating the processing of costmap data into semantically-separated layers. Each layer tracks one type of obstacle or constraint, and then modifies a master costmap which is used for the path planning. We show how the algorithm can be integrated with the open-source ROS navigation stack, and how our approach is easier to fine-tune to specific environmental contexts than the existing monolithic one. Our design also results in faster path planning in practical use, and exhibits a cleaner separation of concerns that the original architecture. The new algorithm also makes it possible to represent complex cost values in order to create navigation behavior for a wide range of contexts.
Year
DOI
Venue
2014
10.1109/IROS.2014.6942636
Intelligent Robots and Systems
Keywords
Field
DocType
path planning,robots,collision free paths,context sensitive navigation,costmap data,environmental contexts,free space,layered costmaps,master costmap,open-source ROS navigation,path planning,path-planning,robot navigation,semantically separated layers,ubiquitous ROS navigation stack
Motion planning,Obstacle,Computer vision,Architecture,Computer science,Robot path planning,Separation of concerns,Free space,Artificial intelligence,Mobile robot navigation,Grid
Conference
ISSN
Citations 
PageRank 
2153-0858
27
1.16
References 
Authors
8
3
Name
Order
Citations
PageRank
David V. Lu1595.37
Dave Hershberger2271.16
Will Smart3763.69