Firefighter Indoor Navigation using Distributed SLAM

Firefighter Indoor Navigation using Distributed SLAM (FINDS)Major Qualifying Project

Matthew ZubielNick Long

Advisers:Prof. Duckworth, Prof. Cyganski1Need for Firefighter LocationWorcester Cold Storage Fire, December 1999In total, 6 firefighters died after becoming lostNeed for outside personnel to keep track of responders indoors

Incident Commanders need current location of each first responderCommunicate directions to firefightersDirect rescue teams to downed firefighter

Photo Credit :Worcester Telegram and Gazette2Technologies of Indoor Navigation and TrackingGPS cannot be used indoorsAlternative ways to track:RF-Based Localization and TrackingInertial Based TrackingDead ReckoningSimultaneous Localization and Mapping (SLAM)Build a map of the environment with no prior knowledge of surroundingsBuild a track of the location of a user3

Simultaneous Localization and MappingEKFMonoSLAM [1]Requires an image set for inputDetects features (corners) in images, and correlates detected corners from frame to frameProduces predictions for both feature location and track4[1] Javier Civera, Oscar G. Grasa, Andrew J. Davison, J. M. M. Montiel, 1-Point RANSAC for EKF Filtering: Application to Real-Time Structure from Motion and Visual dometry, to appear in Journal of Field Robotics, October 2010.

Sample EKFMonoSLAM Output

Our ApproachInitially attempted to develop a real-time tracking systemProcessing time was very longWe attempted to take responsibility off EKFMonoSLAM by implementing functionality remotelyVideo capture and corner detection were moved to a mobile unitMobile unit sent coordinates of detected corners to base station (laptop)5Photo Courtesy Popular ScienceMobile UnitBase Station5Project GoalsCapture and process images in real timeSend resulting data to base station Develop method to provide EKFMonoSLAM algorithm with inputConfigure EKFMonoSLAM algorithm to accurately track motion using corner-only inputRun 2 scenario based tests, and compare experimental results with expected resultsA. Straight Line TestB. 90-Degree Turn

6Mobile Unit Hardware Components2 ComponentsVmodCAM Stereo Camera Module

Atlys FPGA

7Mobile Unit Implementation3 HDL Components:1. Image Capture Data from VmodCAM to rest of design2. Corner Detection Module Detect corners in images from camera.3. Communications Module Transmit corners to base station

8VmodCAM ModuleGather data from VmodCAM to the rest of designI2C Communication for RGB 565 color images Initial Testing using HDMI Display and DDR2 Memory from Digilent Provided Code

9VmodCAMCorner Detection Approach

10Sample Harris Output using MATLAB

VHDL Corner Detection ImplementationPipelined ApproachOperate on each pixel as it arrives

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VHDL Test Bench12

Corner Detection OutputSimulated Input from CameraEthernet ModuleUtilizes Atlys Gigabit Ethernet capabilities and UDP protocolSends corners in the format:Valid, Y-Coordinate, X-Coordinate, Frame NumberSends 360 Corners at a time for data considerations

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Results: Corner DetectionCorner Detection Completed on Atlys FPGA

14Original ImageFPGA Output Results: Corner Detection (cont)15

Complete System Testing2 Scenario Tests PerformedStraight Line and 90-Degree Turn

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Scenario Test Straight Line

17Scenario Test 90 Degree Turn

18ConclusionsGoalImplementationCapture and process images in real timeVModCAM Stereo Camera Module with FPGA ProcessingSend resulting data to base station Ethernet Module on FPGA with Base Station ReceiverDevelop method to provide SLAM algorithm with inputCorner Detection on FPGA to base station receiver (black and white images)Configure SLAM algorithm to accurately track motion using corner-only inputModified settings in EKFMonoSLAM to reflect corner-only inputRun 2 scenario based tests, and compare experimental results with expected resultsVideo results19Successfully tracked the path of a person walking down a corridorSuggestions for Future WorkBefore deployment, many improvements are requiredPower consumption must be analyzed for mobile power implementationEthernet module must be replaced by wireless componentHardware must be ruggedized and form-factor must be minimizedBase station (namely EKFMonoSLAM) needs to be optimized for real-time processing

Possible research into alternate SLAM algorithmsAdditional, more comprehensive scenario testingThermal Camera Expansion20