2. B.M.S COLLEGE OF ENGINEERING
BULL TEMPLE ROAD, BASAVANAGUDI, BANGALORE - 560 019,KARNATAKA, INDIA
2
COMPARATIVE STUDY ON BUSINESS MODEL OF
GOOGLE CHROME AND MOZILLA FIREFOX
Name USN
Sanketh J 1BM19EC143
Sree Suryadatta M Vadhoolas 1BM19EC161
Course Instructor
Prof. Sujatha K
PMF AAT ON
3. 3
INTRODUCTION: GOOGLE CHROME
• Chrome: Free open-source web browser – 2008 by
Google. (Project Chrome)
• Chrome in play store - more than 1 billion
downloads.
• Wide range of extensions and apps: Scribe,
Grammarly, Classroom, Calendar, etc.
• Developer friendly.
• Improved security and performance.
4. 4
INTRODUCTION: MOZILLA FIREFOX
• Firefox: Free open-source web browser – 2002, by
Mozilla. (Project Phoenix)
• Non-profit organization – no
advertising/commercial revenue.
• Revenue – Firefox for organizations, Firefox OS.
• Other products/services – Firefox Focus, Reality,
Lockwise and Send.
• Privacy, security, customization in open source
approach.
6. • Self-driving Cars and ADAS assisted cars need
high level of precision in perception to
achieve safe and accident free road time.
• Navigation in unmapped terrains like oceans,
space and extraterrestrial rovers.
• Industrial/Agricultural mobile robots and
drones.
6
APPLICATIONS OF VO
7. 7
• Challenges:
• To perceive the environment
• To estimate vehicles relative motion from objects
• GNSS does not provide precise Localization. So, SLAM is a viable solution
• SLAM:
• LIDAR based SLAM
• RADAR based SLAM
• Vision based SLAM
• Vision-based SLAM:
• Most cost-effective
• Natural solution
• Problems:
• High computation cost
• Latency for real-time deployment
PROBLEM DEFINITION
8. • Running on CPU:
• Very unpredictable
• Not very efficient
• Running on GPU:
• Cost is High
• Parallelism is only to a certain extent
• Running on FPGA:
• Offer true and reconfigurable parallelism
• Low power
• Cost efficiency
PROBLEM DEFINITION (CONTD..)
9. 9
CAMERA
Image
Sequence
ORB Feature
Extraction
PnP Pose
Estimation
FLANN Feature
Matching
CNN Object
Recognition
Localization and Mapping
PROPOSED SOLUTION
• A Hardware accelerated VO algorithm (subset
of vSLAM) with CNN based object recognition
to realize vSLAM, on PYNQ-Z2 SoC FPGA.
• On FPGA: ORB feature extraction, FLANN
feature matching, PnP pose estimation
• On SoC: Image extraction, Output processing,
CNN based object recognition
• Monocular system improvised to Stereo-vision
system using depth filters (CNN).
10. 10
PROPOSED SOLUTION (CONTD..)
• Accelerator architecture interfaced
to SoC system bus via AXI.
• Access DDR3 memory via DMA
controller.
• Input frames: from SoC via camera /
memory(dataset)
• Output data: sent for visualization to
SoC through AXI interrupts.
System Architecture
12. 12
ESTIMATED BILL OF MATERIAL
Sl.No
.
Manufact
urer
Part
No.
Description Units Unit Cost Cost
1 TUL
DFR06
00
Pynq-Z2 Development
Board*
1 INR 15,173 INR 15,173
2 Logitech C270 USB Camera 1 INR 1,995 INR 1,995
Total Units 2 Total Cost INR 17,168
* PYNQ-Z2 development is obtained from the Department of
Electronics and Communication for this project.
13. • Z. Kuang et al., A Real-time and Robust Monocular Visual Inertial SLAM System Based
on Point and Line Features for Mobile Robots of Smart Cities Toward 6G,IEEE Open
Journal of the Communications Society, vol. 3, 2022
• A. Barzegar, O. Doukhi, D. -J. Lee and Y. -h. Jo, Nonlinear Model Predictive Control for
Self-Driving cars Trajectory Tracking in GNSS-denied environments, ICCAS 2020
• Loo, Shing Yan & Amiri, Ali & Mashohor, Syamsiah & Tang, Sai Hong & Zhang, Hong.
(2018). CNN-SVO: Improving the Mapping in Semi-Direct Visual Odometry Using
Single-Image Depth Prediction
• N. Ragot, R. Khemmar, A. Pokala, R. Rossi and J. -Y. Ertaud, Benchmark of Visual SLAM
Algorithms: ORB-SLAM2 vs RTAB-Map, ICEST 2019
• Wasala, M.; Szolc, H.; Kryjak, T. An Efficient Real-Time FPGA-Based ORB Feature
Extraction for an UHD Video Stream for Embedded Visual SLAM, Electronics 2022
• L. Schäffer, Z. Kincses and S. Pletl, A Real-Time Pose Estimation Algorithm Based on
FPGA and Sensor Fusion, IEEE SISY 2018
REFERENCES
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