How Rough Is the Path? Terrain Traversability Estimation for Local and Global Path Planning

Perception and interpretation of the terrain is essential for robot navigation, particularly in off-road areas, where terrain characteristics can be highly variable. When planning a path, features such as the terrain gradient and roughness should be considered, and they can jointly represent the tra...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2022-09, Vol.23 (9), p.16462-16473
Main Authors: Waibel, Gabriel Gunter, Low, Tobias, Nass, Mathieu, Howard, David, Bandyopadhyay, Tirthankar, Borges, Paulo Vinicius Koerich
Format: Article
Language:English
Subjects:
Autonomous vehicles
Cloud computing
Cost analysis
Costs
intelligent robots
Laser radar
Measurement
mobile robots
Navigation
Neural networks
Path planning
Planning
Point cloud compression
robot learning
Sensors
Terrain analysis
Trajectory
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Summary:Perception and interpretation of the terrain is essential for robot navigation, particularly in off-road areas, where terrain characteristics can be highly variable. When planning a path, features such as the terrain gradient and roughness should be considered, and they can jointly represent the traversability cost of the terrain. Despite this range of contributing factors, most cost maps are currently binary in nature, solely indicating traversible versus non-traversible areas. This work presents a joint local and global planning methodology for building continuous cost maps using LIDAR, based on a novel traversability representation of the environment. We investigate two approaches. The first, a statistical approach, computes terrain cost directly from the point cloud. The second, a learning-based approach, predicts an IMU response solely from geometric point cloud data using a 2D-Convolutional-LSTM neural network. This allows us to estimate the cost of a patch without directly driving over it, based on a data set that maps IMU signals to point cloud patches. Based on the terrain analysis, two continuous cost maps are generated to jointly select the optimal path considering distance and traversability cost for local navigation. We present a real-time terrain analysis strategy applicable for local planning, and furthermore demonstrate the straightforward application of the same approach in batch mode for global planning. Off-road autonomous driving experiments in a large and hybrid site illustrate the applicability of the method. We have made the code available online for users to test the method.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2022.3150328