SLAM - Simultaneous Location and Mapping for Robotics, Part VI

SLAM - Simultaneous Location and Mapping, Part VI

https://www.intechopen.com/source/html/10266/media/image123.jpg

a) In this installment of the SLAM series, we'll examine ROS (Robot Operating System) (https://www.meetup.com/ai-ml-for-robotics-and-iot/messages/boards/thread/50451824) in detail, its concepts, its SLAM facilities, its installation on Raspberry Pi 3, Arduino Mega, and Beaglebone Blue, and basic robotics control programming in the ROS paradigm.

b) finalize the initial capabilities and required components for our own group's SLAM test-bed robot, Blue, that will have a Beaglebone Blue (https://beagleboard.org/blue) as its high-level SLAM and low-level sensor/motion control processor.

*** Here is an excellent resources page (https://github.com/beagleboard/beaglebone-blue/wiki/Accessories) for Beaglebone Blue/

c) continue to discuss the Robotics Club's Sonik (https://www.meetup.com/HoustonRoboticsClub/messages/boards/thread/50978200) and Ariel robots, and member Jim Phalen's Stingray (https://www.meetup.com/ai-ml-for-robotics-and-iot/photos/28042682/) robot as our Raspberry Pi / Arduino Mega-based SLAM test-bed robots.

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10/21/17 - onward - For the subsequent sessions (perhaps more) (one every two weeks), we will build our robots, configure our programming environments, and program our robots with some pretty amazing skills. During this time, we will also track this excellent course presented by Cyrill Stachnis (http://ais.informatik.uni-freiburg.de/teaching/ws13/mapping/), also conveniently organized in this YouTube playlist (https://www.youtube.com/playlist?list=PLgnQpQtFTOGQrZ4O5QzbIHgl3b1JHimN_). (You are not required to read or watch any of the material; however, doing so will prove quite valuable as we progress with building and programming our robots.)

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RESOURCES

View a list of useful resources pertinent to the activities of this group on our Resources Discussion boards (https://www.meetup.com/ai-ml-for-robotics-and-iot/messages/boards/).

To catch up or review, here are summaries (https://www.meetup.com/ai-ml-for-robotics-and-iot/messages/boards/forum/23847483) of what was demoed and discussed at some of our past meetup events.

For other resources for this meetup event, see the links under the main agenda topic below.

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AGENDA

(We'll use this as a guide, but naturally, timing will possibly vary.)

11:00 - Loose discussion period - Recent AI/ML, robotics, IoT news... whatever comes to mind.

11:10 - Welcome message.

11:15 - 5-second personal introductions - Name, current occupation, main interests.

11:20 - Member project showcase - Show and/or discuss what you've been learning and working on. Because of their growing importance, we'll continue to examine Amazon Alexa (Echo), Google Assistant (Home), and other AI Assistants.

11:30 - Review and robotics applications of machine learning topics discussed in the Houston Machine Learning Meetup (https://www.meetup.com/Houston-Machine-Learning/).

11:40 - Main Agenda Topic: Round-table discussion and video presentation of SLAM. For background knowledge, check out the growing push-down list of interesting SLAM-related information links below:

** NEW - A video of one of several low-cost robot configurations for BeagleBone Blue--the EduMIP self-balancing robot: YouTube Video (https://www.youtube.com/watch?v=BIMb8D5RdGA)

** NEW - Another cool robot configuration in the EduMIP line: YouTube Video (https://youtu.be/2rFL9jjawhw?t=4m17s)

An excellent video playlist that details the all-important Kalman Filter: YouTube playlist (https://www.youtube.com/playlist?list=PLX2gX-ftPVXU3oUFNATxGXY90AULiqnWT)

An interesting video-based SLAM project worth dissecting to see how we can apply it to our robots: video (https://www.youtube.com/watch?v=ufvPS5wJAx0), arxiv pdf (https://arxiv.org/pdf/1610.06475.pdf), github source (https://github.com/raulmur/ORB_SLAM2)

Another great SLAM course! This one by Claus Brenner at MIT: YouTube playlist (https://www.youtube.com/watch?v=B2qzYCeT9oQ&list=PLpUPoM7Rgzi_7YWn14Va2FODh7LzADBSm), Python code (https://drive.google.com/drive/folders/0BxwK9_xWk7ewUTFKVEIydTdfMzg?pageId=112650294977066460661)

The excellent course we will be tracking here and on YouTube: course (http://ais.informatik.uni-freiburg.de/teaching/ws13/mapping/), playlist (https://www.youtube.com/playlist?list=PLgnQpQtFTOGQrZ4O5QzbIHgl3b1JHimN_)

A great FREE online class for learning SLAM and other robotics navigation techniques: class (https://www.meetup.com/ai-ml-for-robotics-and-iot/messages/boards/thread/50451679)

You can learn and experiment with SLAM and other robotics localization and navigation techniques completely in simulation using ROS and Gazebo simulation platform. Here is a video that demonstrates that: video demonstration (https://www.youtube.com/watch?v=NRzeQD_Etog), and here are the instructions and resource links to get started: ROS + Gazebo resources (http://www.generationrobots.com/blog/en/2015/02/robotic-simulation-scenarios-with-gazebo-and-ros/)

By popular demand, here's a FUN video playlist for learning Python: playlist (https://www.youtube.com/playlist?list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU). For more Python programming, see this page (https://www.meetup.com/ai-ml-for-robotics-and-iot/messages/boards/thread/50535654) in our Discussion area.

Last, but definitely not least, please spend a minute with Chomba Bupe, an avid AI/ML scientist/enthusiast who graciously shares his knowledge on Quora, to gain fast yet valuable knowledge and awareness of many topics in AI/ML: Chomba Bupe's Quora profile (https://www.quora.com/profile/Chomba-Bupe).

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BREAK

1:00 - Break (or optionally continue on up to the start of the next meetup)

1:30 - Houston Robotics Club (https://www.meetup.com/HoustonRoboticsClub/) meetup - Attendance is optional.