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Autor Tópico: Udacity - Become a Sensor Fusion Engineer (07/2020)  (Lida 416 vezes)

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Udacity - Become a Sensor Fusion Engineer (07/2020)
« em: 22 de Julho de 2020, 18:21 »

Udacity - Become a Sensor Fusion Engineer
WEBRip | English | MP4 + Project Files | 1280 x 720 | AVC ~354 kbps | 29.970 fps
AAC | 128 Kbps | 44.1 KHz | 2 channels | Subs: English (.vtt) | ~12 hours | 2.54 GB
Genre: eLearning Video / Technology, Nanodegree, Engineering
Learn to fuse lidar point clouds, radar signatures, and camera images using Kalman Filters to perceive the environment and detect and track vehicles and pedestrians over time.

ESTIMATED TIME
- 4 Months
- At 10 hours/week

PREREQUISITES
- Intermediate C++, Calculus, and Probability

BUILT IN COLLABORATION WITH

Why take this Nanodegree program

The Sensor Fusion Engineer Nanodegree program will teach you the skills that most engineers learn on-the-job or in a graduate program - how to fuse data from multiple sensors to track non-linear motion and objects in the environment. Apply the skills you learn in this program to a career in robotics, self-driving cars, and much more.

What You Will Learn

Sensor Fusion Engineer

Learn to detect obstacles in lidar point clouds through clustering and segmentation, apply thresholds and filters to radar data in order to accurately track objects, and augment your perception by projecting camera images into three dimensions and fusing these projections with other sensor data. Combine this sensor data with Kalman filters to perceive the world around a vehicle and track objects over time.

Program Offerings

CLASS CONTENT
* Content Co-created with Mercedes-Benz
* Real-world projects
* Project reviews
* Project feedback from experienced reviewers

STUDENT SERVICES
* Technical mentor support- NEW
* Student community - IMPROVED

CAREER SERVICES
* Personal career coaching- NEW
* Interview preparations
* Resume services
* Github review
* LinkedIn profile review

Become a Sensor Fusion Engineer

LEARN
* Combine and filter lidar, radar, and camera data to detect and track vehicles and pedestrians.

AVERAGE TIME
* On average, successful students take 4 months to complete this program.

BENEFITS INCLUDE
* Real-world projects from industry experts
* Technical mentor support
* Personal career coach & career services
* blue stacked bills

* STAY SHARP WHILE STAYING IN
* Financial support available worldwide to help in this challenging time
* Spend your time at home learning new, higher-paying job skills
* Commit to a brighter future by learning today

Why should I enroll?

Sensor fusion engineering is one of the most important and exciting areas of robotics.
Sensors like cameras, radar, and lidar help self-driving cars, drones, and all types of robots perceive their environment. Analyzing and fusing this data is fundamental to building an autonomous system.
In this Nanodegree program, you will work with camera images, radar signatures, and lidar point clouds to detect and track vehicles and pedestrians. By graduation, you will have an impressive portfolio of projects to demonstrate your skills to employers.

Your Instructor

David Silver, Stephen Welch, Andreas Haja, Abdullah Zaidi, Aaron Brown.

   
        General
Complete name                            : 06. ND312 C1 L3 A29 Insert Points - Concept [LB]-kYhS20w1Bnk.mp4
Format                                   : MPEG-4
Format profile                           : Base Media / Version 2
Codec ID                                 : mp42 (isom/mp42)
File size                                : 17.3 MiB
Duration                                 : 4 min 59 s
Overall bit rate mode                    : Variable
Overall bit rate                         : 485 kb/s
Encoded date                             : UTC 2019-03-21 22:00:28
Tagged date                              : UTC 2019-03-21 22:00:28
gsst                                     : 0
gstd                                     : 299467

Video
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Format                                   : AVC
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Codec ID/Info                            : Advanced Video Coding
Duration                                 : 4 min 59 s
Bit rate                                 : 354 kb/s
Width                                    : 1 280 pixels
Height                                   : 720 pixels
Display aspect ratio                     : 16:9
Frame rate mode                          : Constant
Frame rate                               : 29.970 (30000/1001) FPS
Color space                              : YUV
Chroma subsampling                       : 4:2:0
Bit depth                                : 8 bits
Scan type                                : Progressive
Bits/(Pixel*Frame)                       : 0.013
Stream size                              : 12.6 MiB (73%)
Title                                    : ISO Media file produced by Google Inc. Created on: 03/21/2019.
Encoded date                             : UTC 2019-03-21 22:00:28
Tagged date                              : UTC 2019-03-21 22:00:28
Color range                              : Limited
Color primaries                          : BT.709
Transfer characteristics                 : BT.709
Matrix coefficients                      : BT.709

Audio
ID                                       : 2
Format                                   : AAC
Format/Info                              : Advanced Audio Codec
Format profile                           : LC
Codec ID                                 : mp4a-40-2
Duration                                 : 4 min 59 s
Bit rate mode                            : Variable
Bit rate                                 : 128 kb/s
Channel(s)                               : 2 channels
Channel positions                        : Front: L R
Sampling rate                            : 44.1 kHz
Frame rate                               : 43.066 FPS (1024 SPF)
Compression mode                         : Lossy
Stream size                              : 4.57 MiB (26%)
Title                                    : ISO Media file produced by Google Inc. Created on: 03/21/2019.
Language                                 : English
Encoded date                             : UTC 2019-03-21 22:00:28
Tagged date                              : UTC 2019-03-21 22:00:28   

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