International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 10 | Oct 2024 www.irjet.net p-ISSN: 2395-0072
Mitigating Data Noise and Point Cloud Quality Degradation in 3D LiDAR Scanning Technology
Chetan Bhusari1
1
Mechanical Engineer-RTMNU ***
Abstract - LiDAR technology has revolutionized data collection in various industries, including construction, infrastructure, and autonomous vehicles.However,significant issues such as data noise and point cloud quality degradation, especially in challenging environments, persist. These inaccuracies lead to incomplete or erroneous 3D models, impacting project efficiency and decision-making. This paper examines the root causes of data noise in 3D LiDAR scanning and proposes a technical solution to improve data accuracy, including mathematicalmethodsfornoisereductionandpoint cloud optimization.
Key Words: LiDAR, point cloud quality, data noise, surface reflectivity, mathematical noise reduction, signal-to-noise ratio (SNR)
1.INTRODUCTION
LiDAR,orLightDetectionandRanging,isapivotalremote sensingtechnologythatprovidesaccurate3Ddatapointsby measuringthetimeittakesforemittedlaserbeamstoreflect back from surfaces. With applications in sectors such as construction,autonomousvehicles,forestry,andgeographic informationsystems(GIS),LiDARtechnologyhasevolvedto address complex challenges in terrain mapping, object detection,andbuildinginformationmodeling(BIM).
LiDAR (Light Detection and Ranging) systems are widely usedfor3Dmappingandspatialdatacollection.However, despite their precision, environmental and material properties can introduce significant noise into the point cloud data. This noise can result from surface reflectivity, environmentalconditions(e.g.,rain,fog,orintensesunlight), and the laser's range. Managing this noise is critical to improving the reliability of LiDAR data for high-precision applications such as construction site monitoring, urban planning,andinfrastructuredevelopment.
1.1 RESEARCH PROBLEM
This paper explores the challenges associated with data noise and proposes a technical solution based on mathematical noise filtering and advanced sensor fusion techniques.
Theresearchproblemaddressesthechallengeofmitigating noiseandensuringpointclouddataquality.Specifically,this researchinvestigatesvariousmathematical,algorithmic,and
hardware-basedsolutionsforeliminatingorreducingnoise duringdatacaptureandprocessing.Thesesolutionsaimto enhancetheutilityofLiDAR-generatedpointcloudsinhighprecision applications such as structural analysis, automotivenavigation,andgeospatialsurveys.
1.2 OBJECTIVES AND CONTRIBUTIONS
Toidentifyandanalyzetheprimarysourcesofnoisein 3DLiDARscans
To review and evaluate existing noise-mitigation techniques, including statistical filters and machine learningmodels.
To propose an integrated approach that combines hardwareoptimizationwithreal-timenoisecorrection algorithms.
To present real-world case studies where such noise mitigationstrategieshavebeensuccessfullyapplied.
2. ISSUE: DATA NOISE AND POINT CLOUD QUALITY DEGRADATION
2.1
SURFACE REFLECTIVITY AND ABSORPTIVE MATERIALS
Reflectivesurfaces,suchasmetal,glass,orwater,cancause laserbeamstobouncebackincorrectlyleadingtoincorrect datapoints(outliers)inthepointcloud.Absorptivesurfaces, such as dark asphalt, absorb the laser beam, resulting in fewerornoreturns,creatingdatavoids.
Mathematically, the intensity of the returned signal is representedby:
Ir=I0/R2 xρ
Where:
Iristheintensityofthereturnsignal.
I0istheinitialintensityoftheemittedlaser.
Ristherangeordistancebetweenthescannerand thetargetsurface.
ρ is the surface reflectivity coefficient (materialdependent).
Lowreflectivitymaterials(ρ)cancauselowerIr,leadingto missingorinaccuratedatapoints.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 10 | Oct 2024 www.irjet.net p-ISSN: 2395-0072
2.2 ENVIRONMENTAL CONDITIONS
Environmental factors like rain, fog, and dust introduce scattering and absorption of laser pulses, resulting in reducedsignalstrengthandhighernoise. The signal-to-noise ratio (SNR),whichisakeymetricfor measuringdataquality,isgivenby:
SNR=Ps/Pn
Where:
Psisthepowerofthesignal.
Pnisthepowerofthenoise.
Environmental noise Pn increases with adverse weather conditions, decreasing the SNR and leading to poor point cloudquality.Thisissueisparticularlyprominentinoutdoor construction sites where weather conditions fluctuate frequently.
2.3 LONG-RANGE SCANNING
At long distances, laser signals weaken due to the inverse square law, where the power of the returned signal diminisheswiththesquareofthedistance.Thisleadstoan increasingnumberofnoisepointsatthefarendofthescan. TheequationforreceivedsignalpowerPris:
Pr=PtGrGtλ2 /(4πR)2
Where:
Pristhereceivedpower.
Ptisthetransmittedpower.
Gr and Gt are the receiver and transmitter gains, respectively.
λisthewavelengthofthelaser.
Ristherangefromthesensortotheobject.
AsRincreases,Prdecreasesexponentially,contributingto datadegradation.
2.4
INSTRUMENTAL NOISE
Instrumental noise arises from the inherent limitations of theLiDARsystem,includingthelaser'swavelength,sensor sensitivity,andmeasurementaccuracy.The signal-to-noise ratio (SNR) isoftenusedtoquantifythistypeofnoise.
SNR=10log10(PSignal/PNoise)
Where,Psignalisthepowerofthedesiredsignal,andPnoise isthepowerofthebackgroundnoise.
2.5 TEMPORAL AND SPATIAL NOISE
Temporalnoiseoccurswhentime-basedvariationsinlaser emissionsaffectthequalityofreturnedsignals.Spatialnoise refers to inconsistencies in point density across different areas of the scan, which may lead to gaps in data or lowresolutionareas.
2.6 HUMAN INDUCED ERROR
Suboptimal scanner placement and poor coverage during multiple scans can introduce errors. Improper overlap between scans or misalignment can result in poor-quality pointcloudsandnoisydata.
3. PROPOSED TECHNICAL SOLUTION
3.1 MATHEMATICAL NOISE REDUCTION VIA ADAPTIVE FILTERING
Onetechnicalsolutiontoimprovedataaccuracyisthrough adaptive filtering, which dynamically adjusts the filter characteristics based on the noise level. A widely used methodisthe Kalman Filter,whichestimatesthetruevalue ofthemeasureddatabyminimizingtheerrorcovariance.
TheKalmanFilterequationis:
x^k=x^k 1+Kk(zk Hx^k 1)
Where:
x^kistheestimatedstateattimestepkkk
zkisthemeasurementattimestepkkk
Histhemeasurementmatrix.
KkistheKalmangain,computedas:
Kk=Pk-1 HT /HPk-1HT+R
Here, Pk−1is the error covariance matrix and R is the measurementnoisecovariance.
3.2 MULTI-SENSOR FUSION FOR DATA ACCURACY
Another solution to address point cloud noise is by using sensor fusion, where data from multiple sensors (e.g., LiDAR,cameras,andIMUs)arecombinedtoprovideamore robust dataset. This approach enhances data accuracy by compensatingforindividualsensorlimitations.
The Bayesian fusion technique is commonly used to combinedatafromvarioussensors:
P(X∣Z1,Z2)=P(Z1∣X)P(Z2∣X)P(X)/P(Z1)P(Z2)
Where:
P(X∣Z1,Z2),P(X∣Z1,Z2)istheposteriorprobability ofstateXgivenmeasurementsZ1andZ2
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 10 | Oct 2024 www.irjet.net p-ISSN: 2395-0072
P(Z1∣X) P(Z1∣X) and P(Z2∣X) P(Z2∣X) are the likelihoodsofthemeasurementsgiventhestate.
P(X)isthepriorprobabilityofthestate.
Sensorfusionimprovestheoverallaccuracyofpointcloud databymitigatingtheweaknessesofindividualsensors.
3.3 POINT CLOUD DENOISING VIA STATISTICAL OUTLIER REMOVAL (SOR)
The Statistical Outlier Removal (SOR) algorithm helps remove noise by analyzing the distribution of point distances. The points that deviate significantly from their neighborsareconsideredoutliersandareremoved.
Givenapointcloudwithnpoints,foreachpointpi,themean distancedmeantoitsk-nearestneighborsiscomputed:
dmean(pi)=1/K=1∑k∥pi−pj∥
PointswithadmeanvaluethatexceedsacertainthresholdT areconsiderednoiseandareremoved:
Removepointpiifdmean(pi)>T
3.4 VOXEL GRID FILTERING
Voxel grid filtering is a widely used noise reduction techniquethatworksbysubdividingthe3Dspaceintosmall, uniformly sized cubes known as "voxels." Each voxel contains a cluster of LiDAR points, and the points within each voxel are replaced by a single representative point, usuallythecentroidortheaveragepositionofallthepoints withinthatvoxel.Thismethodnotonlyreducesnoise but also helps in compressing the point cloud data, reducing memoryandcomputationalload.
Steps Involved:
The 3D space is divided into a voxel grid of predefinedresolution(e.g.,1cm,10cm).
Pointswithineachvoxelaregroupedtogether,and anewpoint(representative)iscalculatedbasedon thecentroidofthevoxel.
All the points in that voxel are replaced by the centroid,leadingtoadownsampledandsmoothed pointcloud.
3.5 BILATERAL FILTERING
Bilateralfilteringisanonlinearfilteringtechniqueoriginally used in image processing, adapted to point cloud data for noise reduction. It smooth’s the point cloud by averaging neighboring points, but it preserves edges and sharp structuresbytakingbothspatialdistanceandintensity(or another attribute) into account. This method ensures that thefilteronlysmooth’sregionswithsimilarproperties,thus preservingcriticaldetailslikeedges.
Process:
Spatial Proximity: Points close to each other in spacearegivenmoreweight.
Attribute Similarity:Pointswithsimilarintensity values or other attributes are also given more weight.
3.6 RADIUS OUTLIER REMOVER (ROR)
RadiusOutlierRemoval(ROR)isaneffectivetechniquefor eliminatingsparsenoiseinapointcloud.Thisfilterworksby examiningeachpoint’sneighborhood.Ifapointhasfewer neighboringpointswithinadefinedradiusthanaspecified threshold,itisconsideredanoutlierandisremovedfrom the dataset. This technique helps clean up isolated noise points that may be caused by sensor inaccuracies or environmentalfactors.
Definearadiusaroundeachpoint.
Countthenumberofpointswithinthatradius.
If points are less than a predefined threshold, the pointisconsideredanoutlierandremoved.
3.7 MOVING LEAST SQUARES (MLS) SMOOTHING
Moving Least Squares (MLS) is a sophisticated noise reduction technique that aims to smooth point clouds by fittinglocalsurfaces(orpolynomials)toneighborhoodsof points.Unlikesimplermethodslikevoxelgridfiltering,MLS adaptstotheshapeandstructureofthesurface,preserving finedetailswhileeliminatingnoise.Thismethodeffectively addresses uneven surfaces, small imperfections, and measurementerrorswhilepreservingthegeometryofthe underlyingobject.
MLSoperatesbyprojectingeachpointinthecloudonto a locally defined surface that best approximates the neighborhood around that point. The surface is usually definedbyfittinga polynomial function,ensuringthatthe noise is minimized while the overall structure remains accurate.
4. PRACTICAL IMPLEMENTATION
4.1
MULTI-SCAN INTEGRATION AND ICP ALGORITHM
Incaseswhere noisecannot befullyeliminatedthrough a singlescan,multiplescansofthesameareacanbemerged using the Iterative Closest Point (ICP) algorithm. This technique aligns multiple point clouds, reducing random noisebyaveragingthedatafromdifferentperspectives.
4.2 REAL TIME SOLUTIONS
Autonomous systems require real-time noise mitigation. Adaptive techniques dynamically adjust scanning
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 10 | Oct 2024 www.irjet.net p-ISSN: 2395-0072
parameters (e.g., laser intensity) based on environmental feedbacktominimizenoiseduringdataacquisition.
5. CASE STUDIES
5.1
Autonomous Vehicles
AutonomousvehiclesrelyonLiDARforobstacledetection andpathplanning.Real-timenoisemitigationiscriticalfor the accuracy of these systems. Solutions include machine learning models for real-time filtering and multi-scan integrationtoimprovedatafidelity.
5.2 Construction and BIM
In the construction industry, point clouds are used to generateas-builtmodelsofstructures.Noiseinthedatacan lead to inaccuracies in measurements and misalignment withBIMmodels.Multi-scanintegrationandoutlierremoval techniques have been successfully used to mitigate these issues,resultinginmoreaccuratedigitalmodels.
6. Conclusion
Theissueofdatanoiseandpointcloudqualitydegradation in 3D LiDAR scanning can be addressed through a combination of adaptive filtering, sensor fusion, and denoisingalgorithms.Byimplementingmathematicalnoise reduction techniques such as the Kalman Filter and integratingmultiplesensors,theoveralldataaccuracycan besignificantlyimproved.Additionally,applyingpointcloud denoising algorithms such as SOR can help eliminate erroneous data points. These techniques ensure that 3D LiDAR scanning remains reliable even in challenging environments, offering high-precision measurements for onstruction,infrastructure,andotherapplications.
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