Fast and Robust Normal Estimation for Sparse LiDAR Scans
- URL: http://arxiv.org/abs/2404.14281v1
- Date: Mon, 22 Apr 2024 15:29:28 GMT
- Title: Fast and Robust Normal Estimation for Sparse LiDAR Scans
- Authors: Igor Bogoslavskyi, Konstantinos Zampogiannis, Raymond Phan,
- Abstract summary: Mechanical LiDARs rotate a set of rigidly mounted lasers.
One firing of such a set of lasers produces an array of points where each point's neighbor is known.
We use this knowledge to connect these points to their neighbors and label them using the angles of the lines connecting them.
- Score: 1.4952056744888915
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Light Detection and Ranging (LiDAR) technology has proven to be an important part of many robotics systems. Surface normals estimated from LiDAR data are commonly used for a variety of tasks in such systems. As most of the today's mechanical LiDAR sensors produce sparse data, estimating normals from a single scan in a robust manner poses difficulties. In this paper, we address the problem of estimating normals for sparse LiDAR data avoiding the typical issues of smoothing out the normals in high curvature areas. Mechanical LiDARs rotate a set of rigidly mounted lasers. One firing of such a set of lasers produces an array of points where each point's neighbor is known due to the known firing pattern of the scanner. We use this knowledge to connect these points to their neighbors and label them using the angles of the lines connecting them. When estimating normals at these points, we only consider points with the same label as neighbors. This allows us to avoid estimating normals in high curvature areas. We evaluate our approach on various data, both self-recorded and publicly available, acquired using various sparse LiDAR sensors. We show that using our method for normal estimation leads to normals that are more robust in areas with high curvature which leads to maps of higher quality. We also show that our method only incurs a constant factor runtime overhead with respect to a lightweight baseline normal estimation procedure and is therefore suited for operation in computationally demanding environments.
Related papers
- CMRNext: Camera to LiDAR Matching in the Wild for Localization and Extrinsic Calibration [9.693729708337125]
CMRNext is a novel approach for camera-LIDAR matching that is independent of sensor-specific parameters, generalizable, and can be used in the wild.
We extensively evaluate CMRNext on six different robotic platforms, including three publicly available datasets and three in-house robots.
arXiv Detail & Related papers (2024-01-31T19:14:12Z) - NeuralGF: Unsupervised Point Normal Estimation by Learning Neural
Gradient Function [55.86697795177619]
Normal estimation for 3D point clouds is a fundamental task in 3D geometry processing.
We introduce a new paradigm for learning neural gradient functions, which encourages the neural network to fit the input point clouds.
Our excellent results on widely used benchmarks demonstrate that our method can learn more accurate normals for both unoriented and oriented normal estimation tasks.
arXiv Detail & Related papers (2023-11-01T09:25:29Z) - Traj-LO: In Defense of LiDAR-Only Odometry Using an Effective
Continuous-Time Trajectory [20.452961476175812]
This letter explores the capability of LiDAR-only odometry through a continuous-time perspective.
Our proposed Traj-LO approach tries to recover the spatial-temporal consistent movement of LiDAR.
Our implementation is open-sourced on GitHub.
arXiv Detail & Related papers (2023-09-25T03:05:06Z) - Detecting the Anomalies in LiDAR Pointcloud [8.827947115933942]
Adverse weather conditions may cause the LiDAR to produce pointcloud with abnormal patterns such as scattered noise points and uncommon intensity values.
We propose a novel approach to detect whether a LiDAR is generating anomalous pointcloud by analyzing the pointcloud characteristics.
arXiv Detail & Related papers (2023-07-31T22:53:42Z) - Stress-Testing LiDAR Registration [52.24383388306149]
We propose a method for selecting balanced registration sets, which are challenging sets of frame-pairs from LiDAR datasets.
Perhaps unexpectedly, we find that the fastest and simultaneously most accurate approach is a version of advanced RANSAC.
arXiv Detail & Related papers (2022-04-16T05:10:55Z) - LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object
Detection [96.63947479020631]
In many real-world applications, the LiDAR points used by mass-produced robots and vehicles usually have fewer beams than that in large-scale public datasets.
We propose the LiDAR Distillation to bridge the domain gap induced by different LiDAR beams for 3D object detection.
arXiv Detail & Related papers (2022-03-28T17:59:02Z) - Semi-Local Convolutions for LiDAR Scan Processing [0.42970700836450487]
A number of applications, such as mobile robots or automated vehicles, use LiDAR sensors to obtain detailed information about their surroundings.
Many methods use image-like projections to efficiently process these LiDAR measurements and use deep convolutional neural networks to predict semantic classes for each point in the scan.
We propose semi local convolution (SLC), a convolution layer with reduced amount of weight-sharing along the vertical dimension.
arXiv Detail & Related papers (2021-11-30T18:09:43Z) - Learning to Detect Fortified Areas [0.0]
We consider the problem of classifying which areas of a given surface are fortified by for instance, roads, sidewalks, parking spaces, paved driveways and terraces.
We propose an algorithmic solution by designing a neural net embedding architecture that transforms data from all the different sensor systems into a new common representation.
arXiv Detail & Related papers (2021-05-26T08:03:42Z) - StrObe: Streaming Object Detection from LiDAR Packets [73.27333924964306]
Rolling shutter LiDARs emitted as a stream of packets, each covering a sector of the 360deg coverage.
Modern perception algorithms wait for the full sweep to be built before processing the data, which introduces an additional latency.
In this paper we propose StrObe, a novel approach that minimizes latency by ingesting LiDAR packets and emitting a stream of detections without waiting for the full sweep to be built.
arXiv Detail & Related papers (2020-11-12T14:57:44Z) - TadGAN: Time Series Anomaly Detection Using Generative Adversarial
Networks [73.01104041298031]
TadGAN is an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs)
To capture the temporal correlations of time series, we use LSTM Recurrent Neural Networks as base models for Generators and Critics.
To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one.
arXiv Detail & Related papers (2020-09-16T15:52:04Z) - Deep Soft Procrustes for Markerless Volumetric Sensor Alignment [81.13055566952221]
In this work, we improve markerless data-driven correspondence estimation to achieve more robust multi-sensor spatial alignment.
We incorporate geometric constraints in an end-to-end manner into a typical segmentation based model and bridge the intermediate dense classification task with the targeted pose estimation one.
Our model is experimentally shown to achieve similar results with marker-based methods and outperform the markerless ones, while also being robust to the pose variations of the calibration structure.
arXiv Detail & Related papers (2020-03-23T10:51:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.