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Found 10 papers, 1 papers with code
Evidential Semantic Mapping in Off-road Environments with Uncertainty-aware Bayesian Kernel Inference
Robotic mapping with Bayesian Kernel Inference (BKI) has shown promise in creating semantic maps by effectively leveraging local spatial information.
UFO: Uncertainty-aware LiDAR-image Fusion for Off-road Semantic Terrain Map Estimation
Autonomous off-road navigation requires an accurate semantic understanding of the environment, often converted into a bird's-eye view (BEV) representation for various downstream tasks.
In Search of a Data Transformation That Accelerates Neural Field Training
Neural field is an emerging paradigm in data representation that trains a neural network to approximate the given signal.
DA-RAW: Domain Adaptive Object Detection for Real-World Adverse Weather Conditions
Despite the success of deep learning-based object detection methods in recent years, it is still challenging to make the object detector reliable in adverse weather conditions such as rain and snow.
METAVerse: Meta-Learning Traversability Cost Map for Off-Road Navigation
To conduct a comprehensive evaluation, we collect driving data from various terrains and demonstrate that our method can obtain a global model that minimizes uncertainty.
Safe Navigation in Unstructured Environments by Minimizing Uncertainty in Control and Perception
We train a vehicle dynamics model that can quantify the epistemic uncertainty of the model to perform active exploration, resulting in the efficient collection of training data and effective avoidance of uncertain state-action spaces.
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.
Bridging Active Exploration and Uncertainty-Aware Deployment Using Probabilistic Ensemble Neural Network Dynamics
Active exploration, in which a robot directs itself to states that yield the highest information gain, is essential for efficient data collection and minimizing human supervision.
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.
