The recent crisis has increased focus on autonomous robots being used for practical benefit. Weve seen robots cleaning hospitals, delivering food and medicines and even assessing patients. These are all amazing use cases, and clearly illustrate the ways in which robots will play a greater role in our lives from now on.
However, for all their benefits, currently the ability for a robot to autonomously map its surroundings and successfully locate itself is still quite limited. Robots are getting better at doing specific things in planned, consistent environments; but dynamic, untrained situations remain a challenge.
Age of PerceptionWhat excites me is the next generation of SLAM (Simultaneous Localization and Mapping) that will allow robot designers to create robots much more capable of autonomous operation in a broad range of scenarios. It is already under development and attracting investment and interest across the industry.
We are calling it the Age of Perception, and it combines recent advances in machine and deep learning to enhance SLAM. Increasing the richness of maps with semantic scene understanding improves localization, mapping quality and robustness.
Simplifying MapsCurrently, most SLAM solutions take raw data from sensors and use probabilistic algorithms to calculate the location and a map of the surroundings of the robot. LIDAR is most commonly used but increasingly lower-cost cameras are providing rich data streams for enhanced maps. Whatever sensors are used the data creates maps made up of millions of 3-dimensional reference points. These allow the robot to calculate its location.
The problem is that these clouds of 3D points have no meaning they are just a spatial reference for the robot to calculate its position. Constantly processing all of these millions of points is also a heavy load on the robots processors and memory. By inserting machine learning into the processing pipeline we can both improve the utility of these maps and simplify them.
Panoptic SegmentationPanoptic Segmentation techniques use machine learning to categorize collections of pixels from camera feeds into recognizable objects. For example, the millions of pixels representing a wall can be categorized as a single object. In addition, we can use machine learning to predict the geometry and the shape of these pixels in the 3D world. So, millions of 3D points representing a wall can be all summarized into a single plane. Millions of 3D points representing a chair can be all summarized into a shape model with a small number of parameters. Breaking scenes down into distinct objects into 2D and 3D lowers the overhead on processors and memory.
What excites me is the next generation of SLAM that will allow robot designers to create robots much more capable of autonomous operation in a broad range of scenarios. It is already under development and attracting investment and interest across the industry.
Adding UnderstandingAs well as simplification of maps, this approach provides the foundation of greater understanding of the scenes the robots sensors capture. With machine learning we are able to categorize individual objects within the scene and then write code that determines how they should be handled.
The first goal of this emerging capability is to be able to remove moving objects, including people, from maps. In order to navigate effectively, robots need to reference static elements of a scene; things that will not move, and so can be used as a reliable locating point. Machine learning can be used to teach autonomous robots which elements of a scene to use for location, and which to disregard as parts of the map or classify them as obstacles to avoid. Combining the panoptic segmentation of objects in a scene with underlying map and location data will soon deliver massive increases in accuracy and capability of robotic SLAM.
Perceiving ObjectsThe next exciting step will be to build on this categorization to add a level of understanding of individual objects. Machine learning, working as part of the SLAM system, will allow a robot to learn to distinguish the walls and floors of a room from the furniture and other objects within it. Storing these elements as individual objects means that adding or removing a chair will not necessitate the complete redrawing of the map.
This combination of benefits is the key to massive advances in the capability of autonomous robots. Robots do not generalize well in untrained situations; changes, particularly rapid movement, disrupt maps and add significant computational load. Machine learning creates a layer of abstraction that improves the stability of maps. The greater efficiency it allows in processing data creates the overhead to add more sensors and more data that can increase the granularity and information that can be included in maps.
Machine learning can be used to teach autonomous robots which elements of a scene to use for location, and which to disregard as parts of the map or classify them as obstacles to avoid.
Natural InteractionLinking location, mapping and perception will allow robots to understand more about their surroundings and operate in more useful ways. For example, a robot that can perceive the difference between a hall and a kitchen can undertake more complex sets of instructions. Being able to identify and categorize objects such as chairs, desks, cabinets etc will improve this still further. Instructing a robot to go to a specific room to get a specific thing will become much simpler.
The real revolution in robotics will come when robots start interacting more with people in more natural ways. Robots that learn from multiple situations and combine that knowledge into a model that allows them to take on new, un-trained tasks based on maps and objects preserved in memory. Creating those models and abstraction demands complete integration of all three layers of SLAM. Thanks to the efforts of the those who are leading the industry in these areas, I believe that the Age of Perception is just around the corner.
Editors Note: Robotics Business Review would like to thank SLAMcore for permission to reprint the original article (found HERE).
- Machine Learning Answers: Facebook Stock Is Down 20% In A Month, What Are The Chances It'll Rebound? - Trefis - September 22nd, 2020
- Machine Learning in Education Market Incredible Possibilities, Growth Analysis and Forecast To 2025 - The Daily Chronicle - September 22nd, 2020
- Proximity matters: Using machine learning and geospatial analytics to reduce COVID-19 exposure risk - Healthcare IT News - September 22nd, 2020
- Global Machine Learning Market Tends To Show Steady Growth Post Pandemic With Regional Overview and Top Key Players - Verdant News - September 22nd, 2020
- PREDICTING THE OPTIMUM PATH - Port Strategy - September 22nd, 2020
- AI/ML Remains The Most In-Demand Tech Skill Post COVID - Analytics India Magazine - September 22nd, 2020
- Panalgo Brings the Power of Machine-Learning to the Healthcare Industry Via Its IHD Software - AiThority - September 15th, 2020
- Microchip Partners with Machine-Learning (ML) Software Leaders to Simplify AI-at-the-Edge Design Using its 32-Bit Microcontrollers (MCUs) - EE Journal - September 15th, 2020
- What is 'custom machine learning' and why is it important for programmatic optimisation? - The Drum - September 15th, 2020
- PODCAST: NVIDIA's Director of Data Science Talks Machine Learning for Airlines and Aerospace - Aviation Today - September 15th, 2020
- The Use of Machine Learning to Forecast Progression to Advanced AMD - DocWire News - September 15th, 2020
- How Can Machine Learning Help the Teaching Profession? - FE News - September 15th, 2020
- Global Machine Learning in Automobile Market: Development History, Current Analysis and Estimated Forecast to 2024 - The Market Correspondent - September 15th, 2020
- Using machine learning to organize the chemical diversity - Tech Explorist - September 15th, 2020
- Dashboard AI Announces Its Technology Vision for the Foodservice and Hospitality Industry - PRNewswire - September 15th, 2020
- Alfa Releases Second Paper on AI, Using Machine Learning in the Wild - Monitor Daily - September 10th, 2020
- Combatting COVID-19 misinformation with machine learning (VB Live) - VentureBeat - September 10th, 2020
- This artist used machine learning to create realistic portraits of Roman emperors - The World - September 10th, 2020
- Domino Data Lab Named a Leader in Notebook-Based Predictive Analytics and Machine Learning Evaluation by Global Research Firm - Business Wire - September 10th, 2020
- Demonstration Of What-If Tool For Machine Learning Model Investigation - Analytics India Magazine - September 10th, 2020
- RXA to Participate in 2nd Annual A2.AI Conference focused on Machine Learning & Applied AI - PR Web - September 10th, 2020
- 50 Data Science and Analysts Jobs That Opened Just Last Week - Analytics India Magazine - September 10th, 2020
- FSS Launches Next Gen Recon with Machine Learning and Cloud Support - TechGenyz - September 10th, 2020
- Getting to the heart of machine learning and complex humans - The Irish Times - August 28th, 2020
- Global Machine Learning Courses Market Trends, Key Driven Factors, Segmentation And Forecast To 2020-2026 - The Scarlet - August 28th, 2020
- AI and Machine Learning Network Fetch.ai Partners Open-Source Blockchain Protocol Waves to Conduct R&D on DLT - Crowdfund Insider - August 28th, 2020
- UT Austin Selected as Home of National AI Institute Focused on Machine Learning - UT News | The University of Texas at Austin - August 26th, 2020
- Participation-washing could be the next dangerous fad in machine learning - MIT Technology Review - August 26th, 2020
- The Role of Artificial Intelligence and Machine Learning in the... - Insurance CIO Outlook - August 26th, 2020
- Machine Learning Artificial intelligence Market Size and Growth By Leading Vendors, By Types and Application, By End Users and Forecast to 2020-2027 -... - August 26th, 2020
- Air Force Taps Machine Learning to Speed Up Flight Certifications - Nextgov - August 26th, 2020
- What is AutoML and Why Should Your Business Consider It - BizTech Magazine - August 26th, 2020
- Chatbots Are Machine Learning Their Way To Human Language - Forbes - August 26th, 2020
- Explainable AI: From the peak of inflated expectations to the pitfalls of interpreting machine learning models - ZDNet - August 26th, 2020
- Focusing on ethical AI in business and government - FierceElectronics - August 26th, 2020
- Amazon's Machine Learning University To Make Its Online Courses Available To The Public - Analytics India Magazine - August 14th, 2020
- Watch 3 Videos from Coursera's New "Machine Learning for Everyone" - Machine Learning Times - machine learning & data science news - The... - August 14th, 2020
- PhD Research Fellowship in Machine Learning for Cognitive Power Management job with NORWEGIAN UNIVERSITY OF SCIENCE & TECHNOLOGY - NTNU | 219138 -... - August 14th, 2020
- Machine learning is pivotal to every line of business, every organisation must have an ML strategy - BusinessLine - August 14th, 2020
- CORRECTING and REPLACING Anyscale Hosts Inaugural Ray Summit on Scalable Python and Scalable Machine Learning - Yahoo Finance - August 14th, 2020
- Why GPT-3 Heralds a Democratic Revolution in Tech - Built In - August 14th, 2020
- BMW, Red Hat, and Malong Share Insights on AI and Machine Learning During Transform 2020 - ENGINEERING.com - August 14th, 2020
- Algorithm created by deep learning finds potential therapeutic targets throughout the human genome - National Science Foundation - August 14th, 2020
- Ensighten Launches Client-Side Threat Intelligence Initiative and Invests in Machine Learning - WFMZ Allentown - August 6th, 2020
- Hey software developers, youre approaching machine learning the wrong way - The Next Web - August 6th, 2020
- Introducing The AI & Machine Learning Imperative - MIT Sloan - August 6th, 2020
- Who Does the Machine Learning and Data Science Work? - Customer Think - August 6th, 2020
- Artificial Intelligence and Machine Learning Path to Intelligent Automation - Embedded Computing Design - August 6th, 2020
- Blacklight Solutions Unveils Software to Simplify Business Analytics with AI and Machine Learning - PRNewswire - August 6th, 2020
- AI is learning when it should and shouldnt defer to a human - MIT Technology Review - August 6th, 2020
- Moderna Announced Partnership With Amazon Web Services for Their Analytics and Machine Learning Services - Science Times - August 6th, 2020
- Surprisingly Recent Galaxy Discovered Using Machine Learning May Be the Last Generation Galaxy in the Long Cosmic History - SciTechDaily - August 6th, 2020
- STMicroelectronics Releases STM32 Condition-Monitoring Function Pack Leveraging Tools from Cartesiam for Simplified Machine Learning - ELE Times - August 6th, 2020
- Machine Learning Reveals What Makes People Happy In A Relationship - Forbes - August 4th, 2020
- Benefits Of AI And Machine Learning | Expert Panel | Security News - SecurityInformed - August 4th, 2020
- Preparing new machine learning models used to take weeks Activeloop teams up with NVIDIA to reduce that time to hours - MENAFN.COM - August 4th, 2020
- IoT automation trend rides the next wave of machine learning, Big Data - Urgent Communications - August 4th, 2020
- Decoding Practical Problems and Business Implications of Machine Learning - Analytics Insight - August 4th, 2020
- Artificial Intelligence and Machine Learning Industry 2020 Market Manufacturers Analysis, Share, Size, Growth, Trends and Research Report 2026 -... - August 4th, 2020
- Could this software help users trust machine learning decisions? - C4ISRNet - July 27th, 2020
- Top Five Data Privacy Issues that Artificial Intelligence and Machine Learning Startups Need to Know - insideBIGDATA - July 27th, 2020
- COVID-19 Impacts: Machine Learning Market will Accelerate at a CAGR of about 39% through 2020-2024 | The Increasing Adoption of Cloud-based Offerings... - July 27th, 2020
- Deep learning's role in the evolution of machine learning - TechTarget - July 1st, 2020
- 2 books to deepen your command of python machine learning - TechTalks - July 1st, 2020
- What I Learned From Looking at 200 Machine Learning Tools - Machine Learning Times - machine learning & data science news - The Predictive... - July 1st, 2020
- Protecting inventions which use Machine Learning and Artificial Intelligence - Lexology - July 1st, 2020
- Machine learning finds use in creating sharper maps of 'ecosystem' lines in the ocean - Firstpost - July 1st, 2020
- Fake data is great data when it comes to machine learning - Stacey on IoT - July 1st, 2020
- Decisions and NLP Logix Announce Partnership to bring the Power of Machine Learning to Business Process Management - Benzinga - July 1st, 2020
- Machine Learning in Medical Imaging Market Strategies and Insight Driven Transformation 2020-2030 - Cole of Duty - July 1st, 2020
- Impact of COVID-19 Outbreak on Artificial Intelligence and Machine Learning Market to Witness AIBrain, Amazon, Anki, CloudMinds - Cole of Duty - July 1st, 2020
- Machine Learning Market Projected to Register 43.5% CAGR to 2030 Intel, H2Oai - 3rd Watch News - July 1st, 2020
- Learn the business value of AI's various techniques - TechTarget - July 1st, 2020
- Machine Learning As A Service In Manufacturing Market Augmented Expansion to Be Registered by 2018-2023 - 3rd Watch News - July 1st, 2020
- COVID 19 Impact on Machine Learning in Medicine Market Outlook 2020 Industry Size, Top Key Manufacturers, Growth Insights, Demand Analysis and... - July 1st, 2020
- Machine learning algorithm from RaySearch enhances workflow at Swedish radiation therapy clinic - DOTmed HealthCare Business News - July 1st, 2020
- What a machine learning tool that turns Obama white can (and cant) tell us about AI bias - The Verge - June 25th, 2020
- AI and Machine Learning Are Changing Everything. Here's How You Can Get In On The Fun - ExtremeTech - June 25th, 2020
- Googles new ML Kit SDK keeps all machine learning on the device - SlashGear - June 25th, 2020
- Machine Learning vs Predictive Analytics: Are they same? - Analytics Insight - June 25th, 2020