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Found 13 papers, 12 papers with code
Real-Time Safe Control of Neural Network Dynamic Models with Sound Approximation
Safe control of neural network dynamic models (NNDMs) is important to robotics and many applications.
Optimizing LiDAR Placements for Robust Driving Perception in Adverse Conditions
The robustness of driving perception systems under unprecedented conditions is crucial for safety-critical usages.
RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions
Depth estimation from monocular images is pivotal for real-world visual perception systems.
Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbations
The current certification process for assessing robustness is costly and time-consuming due to the extensive number of image projections required for Monte Carlo sampling in the 3D camera motion space.
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
In this paper, we summarize the winning solutions from the RoboDepth Challenge -- an academic competition designed to facilitate and advance robust OoD depth estimation.
Datasets and Benchmarks for Offline Safe Reinforcement Learning
This paper presents a comprehensive benchmarking suite tailored to offline safe reinforcement learning (RL) challenges, aiming to foster progress in the development and evaluation of safe learning algorithms in both the training and deployment phases.
Robustness Certification of Visual Perception Models via Camera Motion Smoothing
To this end, we study the robustness of the visual perception model under camera motion perturbations to investigate the influence of camera motion on robotic perception.
Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Driving
To this end, we introduce an easy-to-compute information-theoretic surrogate metric to quantitatively and fast evaluate LiDAR placement for 3D detection of different types of objects.
A Registration-aided Domain Adaptation Network for 3D Point Cloud Based Place Recognition
In the field of large-scale SLAM for autonomous driving and mobile robotics, 3D point cloud based place recognition has aroused significant research interest due to its robustness to changing environments with drastic daytime and weather variance.
SeasonDepth: Cross-Season Monocular Depth Prediction Dataset and Benchmark under Multiple Environments
Different environments pose a great challenge to the outdoor robust visual perception for long-term autonomous driving, and the generalization of learning-based algorithms on different environments is still an open problem.
DASGIL: Domain Adaptation for Semantic and Geometric-aware Image-based Localization
Long-Term visual localization under changing environments is a challenging problem in autonomous driving and mobile robotics due to season, illumination variance, etc.
Domain-invariant Similarity Activation Map Contrastive Learning for Retrieval-based Long-term Visual Localization
Visual localization is a crucial component in the application of mobile robot and autonomous driving.
Retrieval-based Localization Based on Domain-invariant Feature Learning under Changing Environments
To retrieve a target image from the database, the query image is first encoded using the encoder belonging to the query domain to obtain a domain-invariant feature vector.
