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Found 8 papers, 0 papers with code
Learning Off-Road Terrain Traversability with Self-Supervisions Only
Estimating the traversability of terrain should be reliable and accurate in diverse conditions for autonomous driving in off-road environments.
Self-Supervised 3D Traversability Estimation with Proxy Bank Guidance
To mitigate the uncertainty, we introduce a deep metric learning-based method to incorporate unlabeled data with a few positive and negative prototypes in order to leverage the uncertainty, which jointly learns using semantic segmentation and traversability regression.
ScaTE: A Scalable Framework for Self-Supervised Traversability Estimation in Unstructured Environments
By integrating our framework with a model predictive controller, we demonstrate that estimated traversability results in effective navigation that enables distinct maneuvers based on the driving characteristics of the vehicles.
SLiDE: Self-supervised LiDAR De-snowing through Reconstruction Difficulty
LiDAR is widely used to capture accurate 3D outdoor scene structures.
Distinctiveness Oriented Positional Equilibrium for Point Cloud Registration
Recent state-of-the-art learning-based approaches to point cloud registration have largely been based on graph neural networks (GNN).
Improving Gradient Flow with Unrolled Highway Expectation Maximization
Integrating model-based machine learning methods into deep neural architectures allows one to leverage both the expressive power of deep neural nets and the ability of model-based methods to incorporate domain-specific knowledge.
Gradient-based Camera Exposure Control for Outdoor Mobile Platforms
We introduce a novel method to automatically adjust camera exposure for image processing and computer vision applications on mobile robot platforms.
Vision System and Depth Processing for DRC-HUBO+
This paper presents a vision system and a depth processing algorithm for DRC-HUBO+, the winner of the DRC finals 2015.
