runway centerline deviation estimation from point · pdf file1 runway centerline deviation...

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Runway Centerline Deviation Estimation from Point Clouds using LiDAR imagery

ICRAT 2016Drexel University, Philadelphia, PA ♦ June 20-24, 2016

Seth Young1, Charles Toth2, Zoltan Koppanyi21Department of Civil, Environmental and Geodetic Engineering

The Ohio State UniversityPhone: 614-292-7681

2Satellite Positioning and Inertial Navigation (SPIN) LabCenter for Aviation Studies

E-mail: [email protected]

Outline

Introduction Motivation Remote sensing concept

Laser scanning technologies Sensor selection Initial test results

Tests with taxiing aircraft 1-LiDAR sensor configuration LiDAR point cloud processing

• Methods• Method comparison

Tests with touch&go aircraft 4-LiDAR sensor configuration Results and statistical analysis

Tests with landing aircraft 5-LiDAR sensor configuration Results and statistical analysis

Conclusion/Future work

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Potential

Wingtip clearance,airfield separation

Veer-off models

Tracking take-offs and

landings

• Airport planning: stochastic models.

Source: ACRP Report 51, Risk Assessment Method to Support Modification of Airfield Separation Standards

Light Detection And Ranging

Courtesy of Ayman Habib

INS

Airborne LiDAR/ALS

Mobile LiDAR/MLS

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Google’s driverless car

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Concept

LiDAR Sensor

Trajectory

Outline








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Sensor Comparative Tests

Laser technology is rapidly advancing Application specific sensors are introduced (UAS, autonomous

driving) Performance evaluation needed

to optimize sensor selection to provide reference for low-end sensors

Profilers/scanners considered:

Bridger’s HRS-3D-1W

UTM-30LX-EW

Ibeo Alasca XT

SICK LMS30206 Velodyne HDL-64E

Velodyne HDL-32E

Velodyne VLP-16: 16 channels 100+ m range 300K points/second Dual-return capability 360° x 30° FOV Small size: 100 mm x 65 mm Weight: 600 grams Low power requirements Easy to install No external rotating parts Relatively inexpensive sensor

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Single Scanner Test

Site location: OSU Don Scott Airport The sensors attached to a data acquisition platform (GPSvan) Test aircraft: Cessna 172 Scenarios: aircraft passing diagonally and perpendicularly w.r.t

sensors at various velocities (3, 6, 9,12 knots)

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Test 3: Airplane Georeferencing

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Data Preprocessing

1. Point cloud filtering: remove no aircraft body points, such as runways, buildings, light fixtures, etc.

2. Filtering by time: select those time intervals when aircraft was in the field of view of the sensor

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Methods: Challenges

Sparse point cloud

Difficult to model aircraft bodySample raw data

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Methods under Investigation

1. Center of Gravity (COG) Point cloud center of gravity estimated and tracked

2. ICP-based 2-DoF – 3D ICP (only heading and velocity

estimated)3. Volume minimization (VM) – new approach,

specifically developed for the project Bin-based random optimization (entropy

minimization)

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Method 1: COG

Calculate the center of gravity (COG) of each frame (one point cloud)

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Method 2: Iterative Closest Point

Problem: find the rotation matrix and translation vector that minimize the sum of the distance of the closest points between two points clouds

Source: Point Cloud Library, http://pointclouds.org/documentation/tutorials/int

eractive_icp.php

where 𝑀𝑀 is the 4-by-4 transformation matrix,𝑠𝑠𝑖𝑖 = [𝑥𝑥𝑠𝑠,𝑖𝑖 , 𝑦𝑦𝑠𝑠,𝑖𝑖 , 𝑧𝑧𝑠𝑠,𝑖𝑖 , 1] is the 𝑖𝑖th point from

the samples,𝑟𝑟𝑖𝑖 = [𝑥𝑥𝑠𝑠,𝑖𝑖 , 𝑦𝑦𝑠𝑠,𝑖𝑖 , 𝑧𝑧𝑠𝑠,𝑖𝑖 , 1] is the closest 𝑖𝑖thcoordinates on the reference body curve

Solve this minimization problem between the consecutive frames

min𝛼𝛼,𝛽𝛽,𝛾𝛾,Δ𝑥𝑥,Δy,Δz

�𝑖𝑖=1

𝑁𝑁𝑝𝑝

[𝑀𝑀(𝛼𝛼, 𝛽𝛽, 𝛾𝛾, Δ𝑥𝑥, Δy, Δz) ∗ 𝑠𝑠𝑖𝑖 − 𝑟𝑟𝑖𝑖]2𝑀𝑀 =

𝑟𝑟1,1 𝑟𝑟1,2𝑟𝑟2,1 𝑟𝑟2,2

𝑟𝑟1,3 Δ𝑥𝑥𝑟𝑟2,3 Δ𝑦𝑦

𝑟𝑟3,1 𝑟𝑟3,20 0

𝑟𝑟3,3 Δ𝑧𝑧0 1

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Motion Models

Uniform motion Trajectory is a straight line

𝑥𝑥 = 𝑣𝑣𝑥𝑥𝑡𝑡𝑦𝑦 = 𝑣𝑣𝑦𝑦𝑡𝑡

Curvilinear motion Trajectory is a 2nd order

polynomial

𝑥𝑥 = 𝑣𝑣𝑥𝑥𝑡𝑡 +𝑎𝑎𝑥𝑥

2 𝑡𝑡2

𝑦𝑦 = 𝑣𝑣𝑦𝑦𝑡𝑡 +𝑎𝑎𝑥𝑥

2 𝑡𝑡2

Free motionTrajectory is a higher older

polynomial

𝑥𝑥 𝑡𝑡 = 𝑎𝑎0 + 𝑎𝑎1𝑡𝑡 + 𝑎𝑎2𝑡𝑡2 + ⋯ + 𝑎𝑎𝑛𝑛𝑡𝑡𝑛𝑛,𝑦𝑦 𝑡𝑡 = 𝑏𝑏0 + 𝑏𝑏1𝑡𝑡 + 𝑏𝑏2𝑡𝑡2 + ⋯ + 𝑏𝑏𝑛𝑛𝑡𝑡𝑛𝑛

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Method 3: VM Reconstruction

Problem: reconstruct aircraft body using the equation of motion at various velocities

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Method 3: Volume metric

Decimating space into cubes (voxels); optimized to average point cloud density; 0.5x0.5x0.5 m was used

The “goodness” of the reconstruction is measured by the number of cubes occupied by the reconstruction

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Visual Comparison

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Statistical Comparison

COG may provide good solution but the full aircraft body has to be captured; difficult to achieve

ICP is able to provide acceptable results only in short ranges and when the motion direction w.r.t sensors is close to ~45°; in other cases, it is likely to fail

VM (volume/entropy minimization) gives the best and most robust solution

Outline








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Tests 4 and 5

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System Components

Sensor station With one vertical and one horizontal sensor

Installation (from top to down) Sensor station, GPS antenna, logging laptops, power

GPS AntennaPrecise timing and station coordinates

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Sensor Configuration

Logging laptop

Horizontal sensor

Verticalsensor

GPS Antenna

Battery

Sensor interface boxGPS Receiver

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Network Configuration

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Sample Sensor Data

Landing aircraft

Horizontal sensor axis

Vertical sensor axis

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Typical Raw Data

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Point Clouds

Point clouds acquired by all sensors (top view)

Point clouds acquired by all sensors (rear view)

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Aircraft Variations

Info Sample 1 Sample 2Aircraft type Cessna LearjetOperation Landing TaxiingNumber of sensors 4 2Observation time [s] 7 6.1Observed length [m] ~100 ~27Number of backscattered points 5,970 13,133

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Trajectory Estimation

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Derived Data

Outline








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Tests 6

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Sensor Network Configuration

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Obtaining Reference: Surveying

Goals: Precise centerline reference Precise global coordinates Aiding sensor calibration

Equipment: GPS static solution for control points Total Station for mass points

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Sensor Configuration

Station 2(2 horizontal)Station 1

(2 horizontal,1 vertical)

GPS Antenna

Sensor

Interface Box

GPS Receiver

Logging laptops, batteries

CablingUTP & Power

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Sensor Calibration

Sensor position is known from GPS, but the sensor orientation is unknown

The sensor orientation is estimated during the calibration

Three rotation/attitude angles: roll, pitch, yaw

Very accurate orientation determination is required for precise positioning, as data from various sensors should be integrated

SensorX,Y,Z position is known from GPS

FOV: unknown

FOV: unknown

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Using Planar Target for Sensor and Intra-sensor Calibration

Sensor station

Calibration boardCaptured by the LiDAR

sensors

4 prisms installed at

cornersBoard corners in global system are measured by Total

Station

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Sensor Calibration

Station

Surface normal-basedorientation estimation.

Reference normal is calculated from TS measurements.

Minimize the angle difference between the boards’ and the reference norms.

This is an overdetermined homogenous linear equation system that can be solved by SVD.

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Data Acquisition

Sensor station

Landing airplane

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Method 4: Cube Trajectories

Initial reconstruction from VM

Point association by decimating the space into cubes

The points with different timestamps inside one cube defines the cube’s trajectory

Calculating the cubes’ velocities based on the points

Filtering the velocity time sequence by a Gaussian filter

Trajectory can be derived by integrating the velocity function

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Estimated Velocities

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Results (Video)

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Estimated Altitudes (34 planes)

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Estimated Altitudes (34 planes)

Outline








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Conclusion

LiDAR technology allows tracking taxiing and landing aircraft Single LiDAR sensor configuration may not provide

acceptable results, depending on the sensor quality (affordability)

A multi-LiDAR prototype system developed The interactive target-based sensor network calibration

process provided good accuracy During a real four-hour long test, the landing and/or

taxiong of 34 aircrafts were recorded and processed From the LiDAR point clouds, 2D and 3D trajectories

were estimated ~10 cm level accuracy was obtained; depending on sensor-

object distance, aircraft and sensor types

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Future Work

Assess the reliability and accuracy of the system using GPS reference on aircraft (DGPS, cm-level accuracy)

The optimal sensor network formation and configuration have still open questions

Tests with larger aircraft Very little or no effort has been devoted to investigate the use of

complementary sensors, such as cameras and radar/UWB Point cloud processing has advanced and more sophisticated feature

extraction techniques could be exploited for plane type detection

There have been rapid sensor developments, so the affordability of a sensor configuration for an entire runway is becoming a reality

Ford signed an agreement with Velodynetargeting a $500 unit price

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Thank you for the attention!

Q&A

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