243,279 research outputs found
RELLIS-3D Dataset: Data, Benchmarks and Analysis
Semantic scene understanding is crucial for robust and safe autonomous
navigation, particularly so in off-road environments. Recent deep learning
advances for 3D semantic segmentation rely heavily on large sets of training
data, however existing autonomy datasets either represent urban environments or
lack multimodal off-road data. We fill this gap with RELLIS-3D, a multimodal
dataset collected in an off-road environment, which contains annotations for
13,556 LiDAR scans and 6,235 images. The data was collected on the Rellis
Campus of Texas A&M University, and presents challenges to existing algorithms
related to class imbalance and environmental topography. Additionally, we
evaluate the current state of the art deep learning semantic segmentation
models on this dataset. Experimental results show that RELLIS-3D presents
challenges for algorithms designed for segmentation in urban environments. This
novel dataset provides the resources needed by researchers to continue to
develop more advanced algorithms and investigate new research directions to
enhance autonomous navigation in off-road environments. RELLIS-3D will be
published at https://github.com/unmannedlab/RELLIS-3D
UDTIRI: An Open-Source Road Pothole Detection Benchmark Suite
It is seen that there is enormous potential to leverage powerful deep
learning methods in the emerging field of urban digital twins. It is
particularly in the area of intelligent road inspection where there is
currently limited research and data available. To facilitate progress in this
field, we have developed a well-labeled road pothole dataset named Urban
Digital Twins Intelligent Road Inspection (UDTIRI) dataset. We hope this
dataset will enable the use of powerful deep learning methods in urban road
inspection, providing algorithms with a more comprehensive understanding of the
scene and maximizing their potential. Our dataset comprises 1000 images of
potholes, captured in various scenarios with different lighting and humidity
conditions. Our intention is to employ this dataset for object detection,
semantic segmentation, and instance segmentation tasks. Our team has devoted
significant effort to conducting a detailed statistical analysis, and
benchmarking a selection of representative algorithms from recent years. We
also provide a multi-task platform for researchers to fully exploit the
performance of various algorithms with the support of UDTIRI dataset.Comment: Database webpage: https://www.udtiri.com/, Kaggle webpage:
https://www.kaggle.com/datasets/jiahangli617/udtir
RS2G: Data-Driven Scene-Graph Extraction and Embedding for Robust Autonomous Perception and Scenario Understanding
Human drivers naturally reason about interactions between road users to
understand and safely navigate through traffic. Thus, developing autonomous
vehicles necessitates the ability to mimic such knowledge and model
interactions between road users to understand and navigate unpredictable,
dynamic environments. However, since real-world scenarios often differ from
training datasets, effectively modeling the behavior of various road users in
an environment remains a significant research challenge. This reality
necessitates models that generalize to a broad range of domains and explicitly
model interactions between road users and the environment to improve scenario
understanding. Graph learning methods address this problem by modeling
interactions using graph representations of scenarios. However, existing
methods cannot effectively transfer knowledge gained from the training domain
to real-world scenarios. This constraint is caused by the domain-specific rules
used for graph extraction that can vary in effectiveness across domains,
limiting generalization ability. To address these limitations, we propose
RoadScene2Graph (RS2G): a data-driven graph extraction and modeling approach
that learns to extract the best graph representation of a road scene for
solving autonomous scene understanding tasks. We show that RS2G enables better
performance at subjective risk assessment than rule-based graph extraction
methods and deep-learning-based models. RS2G also improves generalization and
Sim2Real transfer learning, which denotes the ability to transfer knowledge
gained from simulation datasets to unseen real-world scenarios. We also present
ablation studies showing how RS2G produces a more useful graph representation
for downstream classifiers. Finally, we show how RS2G can identify the relative
importance of rule-based graph edges and enables intelligent graph sparsity
tuning
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