Learning Cost-maps Made Easy

09/26/2022
by   Kasi Vishwanath, et al.
0

Cost-maps are used by robotic vehicles to plan collision-free paths. The cost associated with each cell in the map represents the sensed environment information which is often determined manually after several trial-and-error efforts. In off-road environments, due to the presence of several types of features, it is challenging to handcraft the cost values associated with each feature. Moreover, different handcrafted cost values can lead to different paths for the same environment which is not desirable. In this paper, we address the problem of learning the cost-map values from the sensed environment for robust vehicle path planning. We propose a novel framework called as CAMEL using deep learning approach that learns the parameters through demonstrations yielding an adaptive and robust cost-map for path planning. CAMEL has been trained on multi-modal datasets such as RELLIS-3D. The evaluation of CAMEL is carried out on an off-road scene simulator (MAVS) and on field data from IISER-B campus. We also perform realworld implementation of CAMEL on a ground rover. The results shows flexible and robust motion of the vehicle without collisions in unstructured terrains.

READ FULL TEXT

page 1

page 2

page 6

research
01/19/2020

Optimization-Based On-Road Path Planning for Articulated Vehicles

Maneuvering an articulated vehicle on narrow road stretches is often a c...
research
12/06/2020

Conditional Generative Adversarial Networks for Optimal Path Planning

Path planning plays an important role in autonomous robot systems. Effec...
research
09/15/2023

URA*: Uncertainty-aware Path Planning using Image-based Aerial-to-Ground Traversability Estimation for Off-road Environments

A major challenge with off-road autonomous navigation is the lack of map...
research
09/16/2020

Path Planning using Neural A* Search

We present Neural A*, a novel data-driven search algorithm for path plan...
research
06/18/2021

Formation Control with Lane Preference for Connected and Automated Vehicles in Multi-lane Scenarios

Multi-lane roads are typical scenarios in the real-world traffic system....
research
07/27/2021

Critical ride comfort detection for automated vehicles

In a future connected vehicle environment, an optimized route and motion...
research
05/08/2018

Path Evaluation via HMM on Semantical Occupancy Grid Maps

Traditional approaches to mapping of environments in robotics make use o...

Please sign up or login with your details

Forgot password? Click here to reset