89 research outputs found
CNN for IMU Assisted Odometry Estimation using Velodyne LiDAR
We introduce a novel method for odometry estimation using convolutional
neural networks from 3D LiDAR scans. The original sparse data are encoded into
2D matrices for the training of proposed networks and for the prediction. Our
networks show significantly better precision in the estimation of translational
motion parameters comparing with state of the art method LOAM, while achieving
real-time performance. Together with IMU support, high quality odometry
estimation and LiDAR data registration is realized. Moreover, we propose
alternative CNNs trained for the prediction of rotational motion parameters
while achieving results also comparable with state of the art. The proposed
method can replace wheel encoders in odometry estimation or supplement missing
GPS data, when the GNSS signal absents (e.g. during the indoor mapping). Our
solution brings real-time performance and precision which are useful to provide
online preview of the mapping results and verification of the map completeness
in real time
LO-Net: Deep Real-time Lidar Odometry
We present a novel deep convolutional network pipeline, LO-Net, for real-time
lidar odometry estimation. Unlike most existing lidar odometry (LO) estimations
that go through individually designed feature selection, feature matching, and
pose estimation pipeline, LO-Net can be trained in an end-to-end manner. With a
new mask-weighted geometric constraint loss, LO-Net can effectively learn
feature representation for LO estimation, and can implicitly exploit the
sequential dependencies and dynamics in the data. We also design a scan-to-map
module, which uses the geometric and semantic information learned in LO-Net, to
improve the estimation accuracy. Experiments on benchmark datasets demonstrate
that LO-Net outperforms existing learning based approaches and has similar
accuracy with the state-of-the-art geometry-based approach, LOAM
Enabling Multi-LiDAR Sensing in GNSS-Denied Environments: SLAM Dataset, Benchmark, and UAV Tracking with LiDAR-as-a-camera
The rise of Light Detection and Ranging (LiDAR) sensors has profoundly impacted industries ranging from automotive to urban planning. As these sensors become increasingly affordable and compact, their applications are diversifying, driving precision, and innovation. This thesis delves into LiDAR's advancements in autonomous robotic systems, with a focus on its role in simultaneous localization and mapping (SLAM) methodologies and LiDAR as a camera-based tracking for Unmanned Aerial Vehicles (UAV).
Our contributions span two primary domains: the Multi-Modal LiDAR SLAM Benchmark, and the LiDAR-as-a-camera UAV Tracking. In the former, we have expanded our previous multi-modal LiDAR dataset by adding more data sequences from various scenarios. In contrast to the previous dataset, we employ different ground truth-generating approaches. We propose a new multi-modal multi-lidar SLAM-assisted and ICP-based sensor fusion method for generating ground truth maps. Additionally, we also supplement our data with new open road sequences with GNSS-RTK. This enriched dataset, supported by high-resolution LiDAR, provides detailed insights through an evaluation of ten configurations, pairing diverse LiDAR sensors with state-of-the-art SLAM algorithms. In the latter contribution, we leverage a custom YOLOv5 model trained on panoramic low-resolution images from LiDAR reflectivity (LiDAR-as-a-camera) to detect UAVs, demonstrating the superiority of this approach over point cloud or image-only methods. Additionally, we evaluated the real-time performance of our approach on the Nvidia Jetson Nano, a popular mobile computing platform.
Overall, our research underscores the transformative potential of integrating advanced LiDAR sensors with autonomous robotics. By bridging the gaps between different technological approaches, we pave the way for more versatile and efficient applications in the future
DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration
We present DeepICP - a novel end-to-end learning-based 3D point cloud
registration framework that achieves comparable registration accuracy to prior
state-of-the-art geometric methods. Different from other keypoint based methods
where a RANSAC procedure is usually needed, we implement the use of various
deep neural network structures to establish an end-to-end trainable network.
Our keypoint detector is trained through this end-to-end structure and enables
the system to avoid the inference of dynamic objects, leverages the help of
sufficiently salient features on stationary objects, and as a result, achieves
high robustness. Rather than searching the corresponding points among existing
points, the key contribution is that we innovatively generate them based on
learned matching probabilities among a group of candidates, which can boost the
registration accuracy. Our loss function incorporates both the local similarity
and the global geometric constraints to ensure all above network designs can
converge towards the right direction. We comprehensively validate the
effectiveness of our approach using both the KITTI dataset and the
Apollo-SouthBay dataset. Results demonstrate that our method achieves
comparable or better performance than the state-of-the-art geometry-based
methods. Detailed ablation and visualization analysis are included to further
illustrate the behavior and insights of our network. The low registration error
and high robustness of our method makes it attractive for substantial
applications relying on the point cloud registration task.Comment: 10 pages, 6 figures, 3 tables, typos corrected, experimental results
updated, accepted by ICCV 201
LiDAR-Based Place Recognition For Autonomous Driving: A Survey
LiDAR-based place recognition (LPR) plays a pivotal role in autonomous
driving, which assists Simultaneous Localization and Mapping (SLAM) systems in
reducing accumulated errors and achieving reliable localization. However,
existing reviews predominantly concentrate on visual place recognition (VPR)
methods. Despite the recent remarkable progress in LPR, to the best of our
knowledge, there is no dedicated systematic review in this area. This paper
bridges the gap by providing a comprehensive review of place recognition
methods employing LiDAR sensors, thus facilitating and encouraging further
research. We commence by delving into the problem formulation of place
recognition, exploring existing challenges, and describing relations to
previous surveys. Subsequently, we conduct an in-depth review of related
research, which offers detailed classifications, strengths and weaknesses, and
architectures. Finally, we summarize existing datasets, commonly used
evaluation metrics, and comprehensive evaluation results from various methods
on public datasets. This paper can serve as a valuable tutorial for newcomers
entering the field of place recognition and for researchers interested in
long-term robot localization. We pledge to maintain an up-to-date project on
our website https://github.com/ShiPC-AI/LPR-Survey.Comment: 26 pages,13 figures, 5 table
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