GONet++: Traversability Estimation via Dynamic Scene View Synthesis

by   Noriaki Hirose, et al.

Robots that interact with a dynamic environment, such as social robots and autonomous vehicles must be able to navigate through space safely to avoid injury or damage. Hence, effective identification of traversable and non-traversable spaces is crucial for mobile robot operation. In this paper, we propose to tackle the traversability estimation problem using a dynamic view synthesis based framework. View synthesis technique provides the ability to predict the traversability of a wide area around the robot. Unlike traditional view synthesis methods, our method can model dynamic obstacles such as humans, and predict where they would go in the future, allowing the robot to plan in advance and avoid potential collisions with dynamic obstacles. Our method GONet++ is built upon GONet. GONet is only able to predict the traversability of the space right in front of the robot. However, our method applies GONet on the predicted future frames to estimate the traversability of multiple locations around the robot. We demonstrate that our method both quantitatively and qualitatively outperforms the baseline methods in both view synthesis and traversability estimation tasks.


page 5

page 7

page 8


Robot Structure Prior Guided Temporal Attention for Camera-to-Robot Pose Estimation from Image Sequence

In this work, we tackle the problem of online camera-to-robot pose estim...

Affordance-Based Mobile Robot Navigation Among Movable Obstacles

Avoiding obstacles in the perceived world has been the classical approac...

Moving Target Interception Considering Dynamic Environment

The interception of moving targets is a widely studied issue. In this pa...

Domain Adaptation for Outdoor Robot Traversability Estimation from RGB data with Safety-Preserving Loss

Being able to estimate the traversability of the area surrounding a mobi...

Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis

We present a method that simultaneously addresses the tasks of dynamic s...

To Go or Not To Go? A Near Unsupervised Learning Approach For Robot Navigation

It is important for robots to be able to decide whether they can go thro...

To Whom are You Talking? A Deep Learning Model to Endow Social Robots with Addressee Estimation Skills

Communicating shapes our social word. For a robot to be considered socia...

Please sign up or login with your details

Forgot password? Click here to reset