Abstract
The exploration of remote, unknown, rough environments by autonomous robots strongly depends on the ability of the on-board system to build an accurate predictor of terrain traversability. Terrain traversability prediction can be made more cost efficient by using texture information of 2D images obtained by a monocular camera. In cases where the robot is required to operate on a variety of terrains, it is important to consider that terrains sometimes contain spiky objects that appear as non-uniform in the texture of terrain images. This paper presents an approach to estimate the terrain traversability cost based on terrain non-uniformity detection (TNUD). Terrain images undergo a multiscale analysis to determine whether a terrain is uniform or non-uniform. Terrains are represented using a texture and a motion feature computed from terrain images and acceleration signal, respectively. Both features are then combined to learn independent Gaussian Process (GP) predictors, and consequently, predict vibrations using only image texture features. The proposed approach outperforms conventional methods relying only on image features without utilizing TNUD.
Highlights
BackgroundThe autonomous exploration of unstructured environments by mobile robots is witnessing increased interest, as it helps to accomplish diverse tasks such as search and rescue, surveying and data collection (e.g., [1] with unmanned ground vehicles and [2] with unmanned aerial vehicle), and surveillance
We validate the effectiveness of introducing terrain non-uniformity detection (TNUD) to the traversability cost prediction by comparing it with the framework introduced in [24], where the Gaussian process was directly applied to cost prediction without non-uniformity detection
We compute the root-mean-squared prediction error (RMSE), which is defined by v u u1
Summary
The autonomous exploration of unstructured environments by mobile robots is witnessing increased interest, as it helps to accomplish diverse tasks such as search and rescue, surveying and data collection (e.g., [1] with unmanned ground vehicles and [2] with unmanned aerial vehicle), and surveillance. Considering the general use of mobile robots in outdoor environments, it is useful to consider large-scale unevenness, such as rocky and slippy terrains, and small-scale unevenness, which causes vibrations or (vertical) acceleration By considering such small-scale unevenness, it becomes possible to reduce accumulated damage to the robot and instability of its cargo and improve the comfort of passengers
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
