Abstract:
Autonomous navigation is highly important in
robotics, especially when it comes to the robotic applications in
disaster management etc. There are many algorithms to implement
autonomous navigation and most of them are dependent on
prior knowledge of the environment and apriori maps. Although
they are effective in some scenarios, these algorithms fail to
perform when the environment has been subjected to changes
that might invalidate the prior map. This paper presents a point
cloud based algorithm which can be used in a situation where
the prior knowledge of the environment is highly inaccurate. The
proposed algorithm uses depth images to get a local map, which
it expands by searching for uncharted areas picking the next best
location to explore using a breadth first approach given a set of
constraints. The proposed algorithm exploits the maps in the 3D
space allowing the navigation system to perform effectively in
uneven terrains and use inclined planes for its advantage.
Citation:
D. Priyasad et al., "Point Cloud Based Autonomous Area Exploration Algorithm," 2018 Moratuwa Engineering Research Conference (MERCon), 2018, pp. 318-323, doi: 10.1109/MERCon.2018.8421954.