Name
Abstract | ||
|---|---|---|
We propose a simple but effective obstacle- avoiding approach for autonomous robot navigation. The method computes local but safe navigation path and relies only on visual feature information extracted from the environment. To achieve this, we first build a discrete set of candidate navigation points in camera's field of view; then the obstacle avoiding navigation points are selected by evaluating rewards of all candidate points, where the reward metric consists of point-wise transiting probability, safety consideration, mutual information of features, and feature density. Next, we construct a navigable passage in the free space by generating a series of convex hulls that are adjacent to each other. With the navigable passage constructed, a local path that lies within the passage is planned for the robot to safely navigate through. We evaluate the method in both a real world indoor environment as well as a simulated outdoor environment. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/AIPR47015.2019.9174584 | 2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) |
Keywords | DocType | ISSN |
obstacle avoidance,autonomous navigation,autonomous robot navigation,navigation path,visual feature information,discrete set,navigation points,camera,reward metric,point-wise transiting probability,feature density,camera field of view,convex hulls | Conference | 1550-5219 |
ISBN | Citations | PageRank |
978-1-7281-4733-8 | 0 | 0.34 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zheng Chen | 1 | 0 | 0.34 |
| Malintha Fernando | 2 | 0 | 1.01 |
| Lantao Liu | 3 | 157 | 16.49 |