Lidar signal denoising based on wavelet domain spatial filtering

Yin, Shirong; Wang, Weiran

doi:10.1109/icr.2006.343207

Cited by 6 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3) Denoising: LiDAR denoising mainly adopts filtering methods. Some existing works focus on reducing the random and unwanted variations of LiDAR signals in order to obtain the significance of the signal as much as possible, such as wavelet filter [109], mathematical morphology method [110], and Singular Value Decomposition (SVD) approach [111]. However, as mentioned earlier, the signal processing unit of an off-the-shelf LiDAR is usually regarded as a black box.…”

Section: Adverse Weather Conditionsmentioning

confidence: 99%

3D ToF LiDAR in Mobile Robotics: A Review

Yang¹,

Li²,

Zhao³

et al. 2022

Preprint

View full text Add to dashboard Cite

In the past ten years, the use of 3D Time-of-Flight (ToF) LiDARs in mobile robotics has grown rapidly. Based on our accumulation of relevant research, this article systematically reviews and analyzes the use 3D ToF LiDARs in research and industrial applications. The former includes object detection, robot localization, long-term autonomy, LiDAR data processing under adverse weather conditions, and sensor fusion. The latter encompasses service robots, assisted and autonomous driving, and recent applications performed in response to public health crises. We hope that our efforts can effectively provide readers with relevant references and promote the deployment of existing mature technologies in real-world systems.

show abstract

Section: Adverse Weather Conditionsmentioning

confidence: 99%

3D ToF LiDAR in Mobile Robotics: A Review

Yang¹,

Li²,

Zhao³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Input: LiDAR observations y i,j and their SNR values s i,j for 1 ≤ i ≤ m and 1 ≤ j ≤ n (1) i � 1 (for each azimuth angle) (2) for j � 1 to n (along each range) do (3) If s i,j < � − 5, then y i,j � NA (4) end for (5) x i,1 � y i,1 (6)…”

Section: Advances In Meteorologymentioning

confidence: 99%

“…One is for the time-varying but location-fixed LiDAR data. For example, a stationary wavelet domain spatial filteringbased denoising method was proposed by Yin et al [5].…”

Section: Introductionmentioning

confidence: 99%

Observation Selection, Total Variation, and L-Curve Methods for LiDAR Data Denoising

Zhang

Chan

2022

Advances in Meteorology

View full text Add to dashboard Cite

In this article, we propose a light detection and ranging (LiDAR) data denoising scheme for wind profile observation as a part of quality control procedure for wind velocity monitoring and windshear detection. The proposed denoising scheme consists of several components. (i) It selects LiDAR observations according to their SNR values so that serious noisy data can be removed. (ii) A polar-based total variation smoothing term is employed to regularize LiDAR observations. (iii) The regularization parameters are automatically determined to balance the data-fitting term and the total variation smoothing term. Numerical results for LiDAR data collected at the Hong Kong International Airport are reported to demonstrate that the denoising performance of the proposed method is better than that of the testing LiDAR data denoising schemes in the literature.

show abstract

“…In order to obtain environment and target information, echoes need to be further analyzed to get the amplitude, pulse width, and integral intensity information [4]. All the analyzing methods are based on noise suppression, and a good noise suppression effect guarantees effective analysis [5,6].…”

Section: Introductionmentioning

confidence: 99%

Robust Noise Suppression Technique for a LADAR System via Eigenvalue-Based Adaptive Filtering

Xia

Chen

Wang

et al. 2019

Sensors

View full text Add to dashboard Cite

The laser detection and ranging system (LADAR) is widely used in various fields that require 3D measurement, detection, and modeling. In order to improve the system stability and ranging accuracy, it is necessary to obtain the complete waveform of pulses that contain target information. Due to the inevitable noise, there are distinct deviations between the actual and expected waveforms, so noise suppression is essential. To achieve the best effect, the filters’ parameters that are usually set as empirical values should be adaptively adjusted according to the different noise levels. Therefore, we propose a novel noise suppression method for the LADAR system via eigenvalue-based adaptive filtering. Firstly, an efficient noise level estimation method is developed. The distributions of the eigenvalues of the sample covariance matrix are analyzed statistically after one-dimensional echo data are transformed into matrix format. Based on the boundedness and asymptotic properties of the noise eigenvalue spectrum, an estimation method for noise variances in high dimensional settings is proposed. Secondly, based on the estimated noise level, an adaptive guided filtering algorithm is designed within the gradient domain. The optimized parameters of the guided filtering are set according to an estimated noise level. Through simulation analysis and testing experiments on echo waves, it is proven that our algorithm can suppress the noise reliably and has advantages over the existing relevant methods.

show abstract

Lidar signal denoising based on wavelet domain spatial filtering

Cited by 6 publications

References 6 publications

3D ToF LiDAR in Mobile Robotics: A Review

3D ToF LiDAR in Mobile Robotics: A Review

Observation Selection, Total Variation, and L-Curve Methods for LiDAR Data Denoising

Robust Noise Suppression Technique for a LADAR System via Eigenvalue-Based Adaptive Filtering

Contact Info

Product

Resources

About