  We present a novel system, AdVENTR for autonomous robot navigation in
unstructured outdoor environments that consist of uneven and vegetated
terrains. Our approach is general and can enable both wheeled and legged robots
to handle outdoor terrain complexity including unevenness, surface properties
like poor traction, granularity, obstacle stiffness, etc. We use data from
sensors including RGB cameras, 3D Lidar, IMU, robot odometry, and pose
information with efficient learning-based perception and planning algorithms
that can execute on edge computing hardware. Our system uses a scene-aware
switching method to perceive the environment for navigation at any time instant
and dynamically switches between multiple perception algorithms. We test our
system in a variety of sloped, rocky, muddy, and densely vegetated terrains and
demonstrate its performance on Husky and Spot robots.
