1,948 research outputs found
Uses and Challenges of Collecting LiDAR Data from a Growing Autonomous Vehicle Fleet: Implications for Infrastructure Planning and Inspection Practices
Autonomous vehicles (AVs) that utilize LiDAR (Light Detection and Ranging) and other sensing technologies are becoming an inevitable part of transportation industry. Concurrently, transportation agencies are increasingly challenged with the management and tracking of large-scale highway asset inventory. LiDAR has become popular among transportation agencies for highway asset management given its advantage over traditional surveying methods. The affordability of LiDAR technology is increasing day by day. Given this, there will be substantial challenges and opportunities for the utilization of big data resulting from the growth of AVs with LiDAR. A proper understanding of the data size generated from this technology will help agencies in making decisions regarding storage, management, and transmission of the data.
The original raw data generated from the sensor shrinks a lot after filtering and processing following the Cache county Road Manual and storing into ASPRS recommended (.las) file format. In this pilot study, it is found that while considering the road centerline as the vehicle trajectory larger portion of the data fall into the right of way section compared to the actual vehicle trajectory in Cache County, UT. And there is a positive relation between the data size and vehicle speed in terms of the travel lanes section given the nature of the selected highway environment
Benchmarking Deep Learning Architectures for Urban Vegetation Points Segmentation
Vegetation is crucial for sustainable and resilient cities providing various
ecosystem services and well-being of humans. However, vegetation is under
critical stress with rapid urbanization and expanding infrastructure
footprints. Consequently, mapping of this vegetation is essential in the urban
environment. Recently, deep learning for point cloud semantic segmentation has
shown significant progress. Advanced models attempt to obtain state-of-the-art
performance on benchmark datasets, comprising multiple classes and representing
real world scenarios. However, class specific segmentation with respect to
vegetation points has not been explored. Therefore, selection of a deep
learning model for vegetation points segmentation is ambiguous. To address this
problem, we provide a comprehensive assessment of point-based deep learning
models for semantic segmentation of vegetation class. We have selected four
representative point-based models, namely PointCNN, KPConv (omni-supervised),
RandLANet and SCFNet. These models are investigated on three different
datasets, specifically Chandigarh, Toronto3D and Kerala, which are
characterized by diverse nature of vegetation, varying scene complexity and
changing per-point features. PointCNN achieves the highest mIoU on the
Chandigarh (93.32%) and Kerala datasets (85.68%) while KPConv (omni-supervised)
provides the highest mIoU on the Toronto3D dataset (91.26%). The paper develops
a deeper insight, hitherto not reported, into the working of these models for
vegetation segmentation and outlines the ingredients that should be included in
a model specifically for vegetation segmentation. This paper is a step towards
the development of a novel architecture for vegetation points segmentation.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
Efficient 3D Semantic Segmentation with Superpoint Transformer
We introduce a novel superpoint-based transformer architecture for efficient
semantic segmentation of large-scale 3D scenes. Our method incorporates a fast
algorithm to partition point clouds into a hierarchical superpoint structure,
which makes our preprocessing 7 times faster than existing superpoint-based
approaches. Additionally, we leverage a self-attention mechanism to capture the
relationships between superpoints at multiple scales, leading to
state-of-the-art performance on three challenging benchmark datasets: S3DIS
(76.0% mIoU 6-fold validation), KITTI-360 (63.5% on Val), and DALES (79.6%).
With only 212k parameters, our approach is up to 200 times more compact than
other state-of-the-art models while maintaining similar performance.
Furthermore, our model can be trained on a single GPU in 3 hours for a fold of
the S3DIS dataset, which is 7x to 70x fewer GPU-hours than the best-performing
methods. Our code and models are accessible at
github.com/drprojects/superpoint_transformer.Comment: Accepted at ICCV 2023. Camera-ready version with Appendix. Code
available at github.com/drprojects/superpoint_transforme
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Graph-guided Architecture Search for Real-time Semantic Segmentation
Designing a lightweight semantic segmentation network often requires
researchers to find a trade-off between performance and speed, which is always
empirical due to the limited interpretability of neural networks. In order to
release researchers from these tedious mechanical trials, we propose a
Graph-guided Architecture Search (GAS) pipeline to automatically search
real-time semantic segmentation networks. Unlike previous works that use a
simplified search space and stack a repeatable cell to form a network, we
introduce a novel search mechanism with new search space where a lightweight
model can be effectively explored through the cell-level diversity and
latencyoriented constraint. Specifically, to produce the cell-level diversity,
the cell-sharing constraint is eliminated through the cell-independent manner.
Then a graph convolution network (GCN) is seamlessly integrated as a
communication mechanism between cells. Finally, a latency-oriented constraint
is endowed into the search process to balance the speed and performance.
Extensive experiments on Cityscapes and CamVid datasets demonstrate that GAS
achieves the new state-of-the-art trade-off between accuracy and speed. In
particular, on Cityscapes dataset, GAS achieves the new best performance of
73.5% mIoU with speed of 108.4 FPS on Titan Xp.Comment: CVPR202
- …