amit puntambekar advisor: dr. arun lakhotia team cajunbot terrain mapping and obstacle detection...
TRANSCRIPT
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Amit PuntambekarAmit Puntambekar
Advisor: Dr. Arun LakhotiaAdvisor: Dr. Arun Lakhotia
Team CajunBotTeam CajunBot
www.cajunbot.comwww.cajunbot.com
Terrain Mapping and Obstacle Detection Terrain Mapping and Obstacle Detection for Unmanned Autonomous Ground for Unmanned Autonomous Ground Vehicle Without Sensor StabilizationVehicle Without Sensor Stabilization
October 20, 2006October 20, 2006
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Presentation OverviewPresentation Overview
► Introduction and motivation Introduction and motivation ►Related workRelated work►Terrain mapping and obstacle Terrain mapping and obstacle
detection algorithmdetection algorithm►Sensor error handlingSensor error handling►Algorithm evaluationAlgorithm evaluation►Conclusion and future workConclusion and future work
Estimated presentation time: 50 minutes
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DARPA Grand ChallengeDARPA Grand Challenge
►HistoryHistory► Autonomous Autonomous
Ground RobotsGround Robots► Application of AGV’sApplication of AGV’s► ExamplesExamples
Top: Mars Rover by NASA, Bottom: iGator by iRobot
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DARPA Grand ChallengeDARPA Grand Challenge
►Grand Challenge Grand Challenge 20042004
►Grand Challenge Grand Challenge 20052005
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Components of Autonomous Components of Autonomous SystemSystem
►Hardware – Sensors, Electronics, etc.Hardware – Sensors, Electronics, etc.►SoftwareSoftware
Obstacle Detection
Path Planning
Steering
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ObstaclesObstacles
►Man MadeMan Made
►NaturalNatural
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Types of ObstaclesTypes of Obstacles
► Static - Rocks, Cones, Static - Rocks, Cones, Steep Slopes, etc.Steep Slopes, etc.
► Dynamic – Moving Dynamic – Moving Cars, Gate, etc. Cars, Gate, etc.
► Negative Obstacles – Negative Obstacles – Ditches, Potholes, Ditches, Potholes, etc.etc.
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MotivationMotivation
►Timely Obstacle DetectionTimely Obstacle Detection-Top speed of vehicle: 25 mi/hr (11.17 m/s).-Top speed of vehicle: 25 mi/hr (11.17 m/s).
-Even a second delay in detecting obstacle might -Even a second delay in detecting obstacle might be fatal.be fatal.
►Static and Dynamic ObstaclesStatic and Dynamic Obstacles
►Negative ObstaclesNegative Obstacles
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SensorsSensors
►GPSGPS Position informationPosition information
► INSINS OrientationOrientation
►LIDARLIDAR RangeRange
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GPS/INSGPS/INS
► Principle of Principle of OperationOperation
►Data Format Data Format ► Erroneous Erroneous
ConditionsConditions
100 Hz
Position
5 Hz
Position +
Orientation
GPS INS
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LIDARLIDAR
0 degree0 degree
180 degree180 degree
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LIDAR TerminologiesLIDAR TerminologiesLaser Beams
RangeRange► LIDARLIDAR BeamsBeams
ScanScan
• 1 Scan = 180 1 Scan = 180 beamsbeams
• 75 Scans per Sec75 Scans per Sec
Time Time StampStamp
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LIDAR MountingLIDAR Mounting
Parallel to GroundParallel to Ground
Used by CMUUsed by CMU
Minimum obstacle Minimum obstacle sizesize
Slopes as obstaclesSlopes as obstacles
Sensitive to Sensitive to
VibrationsVibrations
MountingsMountings
Air PressureAir Pressure
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LIDAR MountingLIDAR Mounting
► Sweeping the Sweeping the terrainterrain Scans sweep terrainScans sweep terrain Successive scans are Successive scans are
geographically closegeographically close
Consecutive scans on flat Consecutive scans on flat groundground
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LIDAR MountingLIDAR Mounting
►Vertical MountingVertical Mounting Team GRAYTeam GRAY Data Data
DiscontinuityDiscontinuity
►Combination MountingCombination Mounting
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Algorithms for Sweeping Algorithms for Sweeping LIDARsLIDARs
► Consecutive scan Consecutive scan analysisanalysis The laser scans incrementally sweep the The laser scans incrementally sweep the
surfacesurface Analyzing the consecutive scans to Analyzing the consecutive scans to
determine change in geometry of the terraindetermine change in geometry of the terrain
► Data DiscontinuityData Discontinuity Detect Discontinuity in dataDetect Discontinuity in data Team GRAY, METIORTeam GRAY, METIOR
► Plane FittingPlane Fitting Best fitting plane computationBest fitting plane computation Virginia TechVirginia Tech
► Slope ComputationSlope Computation Change in slope of the scans is computedChange in slope of the scans is computed CMU, Team ENSCOCMU, Team ENSCO
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Prior Work ReviewPrior Work Review
►Dependent on Dependent on Incremental scan sweepingIncremental scan sweeping
►Flat terrainFlat terrain
Sensor mountingsSensor mountings►Will not work if sensor mounting is changedWill not work if sensor mounting is changed
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Off-Road Conditions- BumpsOff-Road Conditions- Bumps
► Indoor Vs. Off-Indoor Vs. Off-road road EnvironmentsEnvironments
Effect of BumpsEffect of Bumps
Scattered scans due to bumpsScattered scans due to bumps
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Sensor StabilizationSensor Stabilization
► Specific Sensor Specific Sensor StabilizersStabilizers
► Vehicle SuspensionsVehicle Suspensions
Top: Sandstorm from CMU; Bottom: IRV from Indiana Robotics
22 out of the 23 2005 Grand Challenge finalist team had vehicle suspensions or hardware sensor stabilizers to mitigate bumps.
Teams like CMU, IRV had both
CajunBot was the only entry without sensor stabilizer and suspensions
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Sensor StabilizersSensor Stabilizers
► CostCost - The cost of the CMU Gimbal - The cost of the CMU Gimbal
is approximately $70,000.is approximately $70,000.
► Single Point of Single Point of FailureFailure
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Research ContributionResearch Contribution
►Off-Road Obstacle Detection SystemOff-Road Obstacle Detection System Without sensor stabilizationWithout sensor stabilization Not sensitive to sensor mountingsNot sensitive to sensor mountings Accounts for GPS errorsAccounts for GPS errors Scales well with number of sensorsScales well with number of sensors
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Core AlgorithmCore Algorithm
►Obstacle Detection AlgorithmObstacle Detection Algorithm TheoryTheory Implementation for a real time system – Implementation for a real time system –
CajunBotCajunBot Error HandlingError Handling
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Algorithm TheoryAlgorithm Theory
PointsPoints Triangle FormationTriangle Formation Slope Slope ComputationComputation
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Algorithm Theory Algorithm Theory (Continued)(Continued)
High Absolute SlopeHigh Absolute Slope
High Relative SlopeHigh Relative Slope
Height DiscontinuityHeight Discontinuity
ObstacleObstacle
Triangle AnalysisTriangle Analysis
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Obstacle DetectionObstacle Detection
► High Absolute SlopeHigh Absolute Slope- - Large surfaces where triangles Large surfaces where triangles
can can be formed be formed Eg: Wall, cars, etc.Eg: Wall, cars, etc.
► High Relative SlopeHigh Relative Slope- - Obstacles on slopeObstacles on slope- When obstacles are not large - When obstacles are not large
enough to register enough to register three LIDAR three LIDAR beams to form beams to form trianglestriangles
- Negative obstacles- Negative obstacles
► High Elevation ChangeHigh Elevation Change- - Narrow obstacles like polesNarrow obstacles like poles
- - Negative ObstaclesNegative Obstacles
angle
Top: Virtual Triangle on a wall like obstacle
Bottom: Obstacle on a slope
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Real Time SystemReal Time System
+Data Fusion
(Range, Angle),75
Position
. . . .. . . .
. . . .. . . .
. . . .. . . .
. . . .. . . .
Slope ComputationSlope Computation
Pos +Pos +
OrientatioOrientationn
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Effect of BumpsEffect of Bumps
Scans Scattered due to Scans Scattered due to bumpsbumps
Consecutive scans might be Consecutive scans might be geographically far apartgeographically far apart
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Spatial GridingSpatial Griding
+
.. . .. ....
…… .. .. .
.. .. .... .
. .. .. ..Slope Grid
Data Fusion
(Range, Angle),75
Position, Location
Position
. . .. . .
. . . . ..
. . .. . .
. . .. . .
Slope computationSlope computation
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Data ConsistencyData Consistency Temporal Temporal
accuracy of GPS accuracy of GPS GPS DriftGPS Drift
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22
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Sensor Error - GPS DriftSensor Error - GPS Drift
►What is GPS What is GPS Drift ?Drift ? Gradual drift in the GPS dataGradual drift in the GPS data
►Effects of GPS Effects of GPS Drift?Drift? Only temporally close data Only temporally close data
can be comparedcan be compared
►Factors causing Factors causing GPS DriftGPS Drift Hardware and connectivity Hardware and connectivity
with satelliteswith satellitesGraph: GPS Z Vs. Time
Stationary
Moving
Data Collected on a flat parking lot. Vehicle traveling at 3m/s
X Axis: Time (s)
Y Axis: Height (m)
0.130.13
0.200.20
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Handling GPS DriftHandling GPS Drift
►Temporal Temporal Data OrderingData Ordering
►GPS stable for GPS stable for 3-4 seconds3-4 seconds
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Obstacle DetectionObstacle DetectionObstacle Cell AnalysisObstacle Cell Analysis
Absolute SlopeAbsolute Slope
Relative SlopeRelative Slope
Height Discontinuity Height Discontinuity
Terrain Obstacle Map (TOM) GridTerrain Obstacle Map (TOM) Grid
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Obstacle Detection – TOM Obstacle Detection – TOM Grid AnalysisGrid Analysis
…… .... …… ....
.. .... ……....
.... .. .. ..
.. …… ....
Terrain Obstacle Map
► High Absolute SlopeHigh Absolute SlopeAbsolute Orientation > Absolute Orientation >
ThresholdThreshold
► High Relative SlopeHigh Relative SlopeDifference in Orientation > Difference in Orientation >
Threshold Threshold &&Difference in Height > ThresholdDifference in Height > Threshold
► High Elevation High Elevation ChangeChange
Difference in Height > ThresholdDifference in Height > Threshold
(Max Orientation,
Min Orientation,
Max Height,
Min Height,
Num of Centroids,
Num of Hits)
Potential Obstacle Determination
Confidence Factors
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Dynamic ObstaclesDynamic Obstacles
►Dynamic ObstaclesDynamic Obstacles registered as registered as
obstacle at every obstacle at every locationlocation
Refresh GridRefresh Grid►Grid RefreshingGrid Refreshing
- TOM in a spatio-- TOM in a spatio-temporal temporal grid grid
- Refresh TOM Cells if - Refresh TOM Cells if existing data existing data
and new data and new data are not are not temporally closetemporally close
- Aging based on - Aging based on access time access time stamp stamp
…… .... …… ....
.. .... ……....
.... .. .. ..
.. …… ....
Last Access Time
Stamp,
T_old
New Data, T_new
Terrain Obstacle Map (TOM)
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Sensor ErrorSensor ErrorGPS SpikeGPS Spike
►What is GPS Spike ?What is GPS Spike ?-Sudden change in -Sudden change in
the GPS data in a very the GPS data in a very short time interval.short time interval.
-The elevation data -The elevation data is more prone to GPS is more prone to GPS SpikesSpikes
► Causes for GPS Causes for GPS SpikeSpike
-Weak Signal-Weak Signal
-After ‘Dead -After ‘Dead Reckoning’Reckoning’
X-axis : Time(s)
Y-axis: Height(m)
Graph: GPS (Z) Vs. Time
NQE 2005 Data
3030mm
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Effect of GPS Spike Effect of GPS Spike
GPS Spike Data PlaybackGraph: GPS Z Vs. Time
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GPS Spike: Reason for False GPS Spike: Reason for False ObstaclesObstacles
Corrupted data enters systemCorrupted data enters system
Slope computation gets Slope computation gets erroneouserroneous
Data FilterData Filter
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Detecting GPS SpikeDetecting GPS Spike
Median filter monitors Median filter monitors INS DataINS Data
Erroneous data is Erroneous data is discardeddiscarded
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Core Algorithm- RevisedCore Algorithm- Revised
► Terrain ModelingTerrain Modeling►Obstacle Obstacle
DetectionDetection
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Effect of Bumps - IIEffect of Bumps - II
► INS, LIDAR data fusionINS, LIDAR data fusion-Mounting INS on -Mounting INS on LIDAR LIDAR-Good Suspensions-Good Suspensions-Sensor Stabilizers-Sensor Stabilizers-Rigid Platform-Rigid Platform
LIDARS
GPS
INS
Rigid Platform
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Effects of BumpsEffects of Bumps
► INS, LIDAR Data Rate MismatchINS, LIDAR Data Rate Mismatch
t1 t3
Time
AngleLIDAR
INS00
00 XX
t2t2
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Sensor FusionSensor Fusion
► CBWare - Data CBWare - Data interpolation interpolation supportsupport
► Robots with Robots with sensor stabilizers sensor stabilizers can fuse the most can fuse the most recent data from recent data from sensorssensors
► In CajunBot data In CajunBot data is interpolated is interpolated based on time of based on time of productionproduction
t1 t2
Time
Angle
LIDAR
INS
XX
InterpolatioInterpolationn
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Algorithm EvaluationAlgorithm Evaluation
►Ability to utilize bumps to see furtherAbility to utilize bumps to see further►Accuracy of resultsAccuracy of results►Algorithm complexityAlgorithm complexity►ScalabilityScalability►Different obstacle typesDifferent obstacle types►Sensor orientation independenceSensor orientation independence
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Data SetsData Sets
►Logged data from 2005 GCLogged data from 2005 GC►Testing in a controlled environment Testing in a controlled environment
with CajunBot-IIwith CajunBot-II►Testing in a simulated environment - Testing in a simulated environment -
CBSimCBSim
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Evaluation – Effects of BumpsEvaluation – Effects of Bumps► Obstacle detection Obstacle detection
distance increases linearly distance increases linearly with severity of bumps with severity of bumps experiencedexperienced
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Testing in a controlled Testing in a controlled environment with CajunBot-II environment with CajunBot-II
Effect of bumps Effect of bumps
With With BumpsBumps
Without Without BumpsBumps
DistanceDistance 42.6 m42.6 m 28.5 m28.5 m
False False ObstaclesObstacles
NILNIL NILNIL
Experimental Setup Obstacle detection without Bumps
Obstacle detection with bumpsComparison table
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ScalabilityScalability
Sensor Specific Computation
Data Specific Computation
Results based on analyzing CajunBot-II logged data on a Dell machine with 3.2 GHz Intel
Processor and 1 GB RAM with full load (all other CajunBot software modules running) on Fedora
Core 2 operating system
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Scalability and Bumps Scalability and Bumps
Results based on analyzing the 2005 GC final run logged data on a Dell machine with 1.6 GHz Intel Processor and 1 GB RAM with full load (all other CajunBot software modules running) on Fedora Core 2 operating system
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Sensor Orientation Sensor Orientation IndependenceIndependence
CajunBot with two distinct sensor orientations
Run 1Run 1
Top sensor Top sensor
Orientation (r, p, h)=(0, -1.5, Orientation (r, p, h)=(0, -1.5, 1)1)
Offsets (X, Y, Z)=(0.25, 1, Offsets (X, Y, Z)=(0.25, 1, 0.25)0.25)
Bottom Sensor Bottom Sensor
Orientation (r, p, h)=(0, -3, Orientation (r, p, h)=(0, -3, 1.5)1.5)
Offsets (X, Y, Z)=(0.25, 1.5, -Offsets (X, Y, Z)=(0.25, 1.5, -0.5)0.5)
Run 2Run 2
Top sensor Top sensor
Orientation (r, p, h)=(2, -4.5, Orientation (r, p, h)=(2, -4.5, 0)0)
Offsets (X, Y, Z)=(0, 1, 0.5)Offsets (X, Y, Z)=(0, 1, 0.5)
Bottom Sensor Bottom Sensor
Orientation (r, p, h)=(-2, -4, Orientation (r, p, h)=(-2, -4, 2)2)
Offsets (X, Y, Z)=(0, 2, 0)Offsets (X, Y, Z)=(0, 2, 0)
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Comparison with different Comparison with different obstacle shapes at varying obstacle shapes at varying
speedspeed
HD: Height Discontinuity, AS: Absolute Slope, RS: Relative Slope
Considerable increase in speed, negligible decrease in efficiency
4%, 3.2%, 3.9% decrease in detection distance among the three shapes when the speed increases by 150 %
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Limitations of the algorithmLimitations of the algorithm
►High precision INS requiredHigh precision INS required►Sensitive to Boresight MisalignmentSensitive to Boresight Misalignment
Angle at which LIDAR is mounted w.r.t INSAngle at which LIDAR is mounted w.r.t INS Fusion data from multiple LIDARsFusion data from multiple LIDARs
►Limited LIDAR reflectivityLimited LIDAR reflectivity WaterWater Black surfaces – tar roads, etc.Black surfaces – tar roads, etc.
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ConclusionConclusion
An Obstacle Detection system An Obstacle Detection system Without sensor stabilizationWithout sensor stabilization
►Takes advantage of bumps to see furtherTakes advantage of bumps to see further Not sensitive to sensor mountingsNot sensitive to sensor mountings Accounts for GPS errorsAccounts for GPS errors Scales well with number of sensorsScales well with number of sensors Handles dynamic obstaclesHandles dynamic obstacles
Efficient Spatio-Temporal Grid Efficient Spatio-Temporal Grid Evaluation of systemEvaluation of system
On logged data, live on CajunBot-II, and On logged data, live on CajunBot-II, and simulatorsimulator
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Future WorkFuture Work
►Dynamic obstacle detectionDynamic obstacle detection Detecting trajectory and speedDetecting trajectory and speed
►Obstacle ClassificationObstacle Classification Vegetation, mesh, etc Vegetation, mesh, etc
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Questions ?Questions ?
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Thank youThank you
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Backup SlidesBackup Slides
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Boresight Angles / OffsetsBoresight Angles / Offsets
H
Hs
GPS
x
p
n
y
INS
Body on which the sensor is mounted
Laser sensor
Laser beam
Actual Ground Line
Computed Ground Line
R
P
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Limitations of the algorithmLimitations of the algorithm
►High precision INS requiredHigh precision INS required►Sensitive to Boresight MisalignmentSensitive to Boresight Misalignment
Angle at which LIDAR is mounted w.r.t INSAngle at which LIDAR is mounted w.r.t INS Fusion data from multiple LIDARsFusion data from multiple LIDARs
►Water, black surfaces do not reflect Water, black surfaces do not reflect LIDARLIDAR
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LIDAR ErrorsLIDAR Errors
► Boresight Boresight MisalignmentMisalignment
L1
L2
L3
L4
L5
L7
L6
Effect of Mounting Angles on Sensor Data
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Sensors UsedSensors Used
► LIDARsLIDARs
►GPSGPS
► INSINS
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GPS/INSGPS/INS
► Principle of Principle of OperationOperation
►Data Format Data Format ► Erroneous Erroneous
ConditionsConditions
100 Hz
Position
5 Hz
Position +
Orientation
GPS INS
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LIDARSLIDARS► Principle of Operation Principle of Operation
– Time of Flight– Time of Flight►Data Format – 75 Hz Data Format – 75 Hz
at 0.25 degree offsetat 0.25 degree offset► Erroneous ConditionsErroneous Conditions
Laser Beams
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Data HandlingData Handling
► To keep multiple To keep multiple copies of 2 minute copies of 2 minute worth of data in worth of data in memory would memory would require 550 MB of require 550 MB of RAMRAM
► Updating multiple Updating multiple copies is expensivecopies is expensive
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Data HandlingData Handling
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LIDAR TerminologiesLIDAR Terminologies
LIDAR BeamsLIDAR Beams
. . . . . . . . . . ..
Laser ScanLaser Scan
1 scan 1 scan == 180 beams180 beams
75 scans per sec75 scans per sec