sensor fusion

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pages: 472 words: 80,835

Life as a Passenger: How Driverless Cars Will Change the World by David Kerrigan

3D printing, Airbnb, airport security, Albert Einstein, autonomous vehicles, big-box store, butterfly effect, call centre, car-free, Cesare Marchetti: Marchetti’s constant, Chris Urmson, commoditize, computer vision, congestion charging, connected car, DARPA: Urban Challenge, deskilling, disruptive innovation, edge city, Elon Musk,, future of work, invention of the wheel, Just-in-time delivery, loss aversion, Lyft, Marchetti’s constant, Mars Rover, megacity, Menlo Park, Metcalfe’s law, Minecraft, Nash equilibrium, New Urbanism, QWERTY keyboard, Ralph Nader, RAND corporation, Ray Kurzweil, ride hailing / ride sharing, Rodney Brooks, Sam Peltzman, self-driving car, sensor fusion, Silicon Valley, Simon Kuznets, smart cities, Snapchat, Stanford marshmallow experiment, Steve Jobs, technoutopianism, the built environment, Thorstein Veblen, traffic fines, transit-oriented development, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, Unsafe at Any Speed, urban planning, urban sprawl, Yogi Berra, young professional, zero-sum game, Zipcar

These need to be combined to achieve a solution that can begin to be credible as a substitute for human control. Sensor Fusion As human drivers, we rely primarily on our eyes for inputs, while our brains analyse the situation and direct the muscles in our legs and arms to control the car via the steering wheel and pedals. For automation, sending the commands to the steering, braking and acceleration controls is technically very easy. The hard part is “seeing” the world around it, interpreting it correctly and then proceeding safely. There is no one type of electronic sensor that provides all the inputs needed to drive as efficiently as the human eye, so most driverless cars rely on an approach of adding multiple types of sensor to vehicles and combining their inputs using a technique called Sensor Fusion. The following illustration shows a prototype driverless car, and the position of the various sensors that contribute to the overall ability of the car to “see”.

All the sensors on these vehicles have been engineered and manufactured in-house by Waymo,[108] and include three types of LiDAR that operate at short, medium and long ranges, and the upgraded system on the Pacifica features eight enhanced camera modules and an additional high-resolution forward-looking multi-sensor module designed to be able to detect smaller objects like traffic cones at longer distances. Much like Apple carefully controls its customer experience because it builds both the software and the hardware for its product suite, Waymo can claim tighter integration between its sensor hardware, sensor fusion software, image recognition and other aspects of its self-driving system. Waymo also claims individual performance benefits in each of its new sensors, including vision cameras, radars and LiDAR, saying each provides better resolution, sensing distance and accuracy than the hardware it has been using on prior vehicles.[109] Show Me the Money Some idea of the scope of change we’re talking regarding the advent of driverless cars is evident given the investment that one of the world’s smartest companies have chosen to put into it.

pages: 202 words: 59,883

Age of Context: Mobile, Sensors, Data and the Future of Privacy by Robert Scoble, Shel Israel

Albert Einstein, Apple II, augmented reality, call centre, Chelsea Manning, cloud computing, connected car, Edward Snowden, Edward Thorp, Elon Musk, factory automation, Filter Bubble, G4S, Google Earth, Google Glasses, Internet of things, job automation, John Markoff, Kickstarter, lifelogging, Marc Andreessen, Mars Rover, Menlo Park, Metcalfe’s law, New Urbanism, PageRank, pattern recognition, RFID, ride hailing / ride sharing, Robert Metcalfe, Saturday Night Live, self-driving car, sensor fusion, Silicon Valley, Skype, smart grid, social graph, speech recognition, Steve Jobs, Steve Wozniak, Steven Levy, Tesla Model S, Tim Cook: Apple, ubercab, urban planning, Zipcar

An environmentally friendly company, Sensible Self, makes GreenGoose, cute little wireless stickers containing motion sensors that allow you to track anything that moves, from a pet or child to your phone, or even to check if your spouse left the toilet seat up. Melanie Martella, Executive Editor of Sensors magazine, introduced us to the concept of sensor fusion, a fast-emerging technology that takes data from disparate sources to come up with more accurate, complete and dependable data. Sensor fusion enables the same sense of depth that is available in 3D modeling, which is used for all modern design and construction, as well as the magic of special effects in movies. Sensors will understand if you are pilfering office supplies or engaging in a clandestine office affair. If you are a burglar, your phone might end up bearing witness against you and, in fact, your car will be able to testify if you were parked in an area you deny having visited—and it will be able to report when you were there, and if it was you in the car.

pages: 523 words: 61,179

Human + Machine: Reimagining Work in the Age of AI by Paul R. Daugherty, H. James Wilson

3D printing, AI winter, algorithmic trading, Amazon Mechanical Turk, augmented reality, autonomous vehicles, blockchain, business process, call centre, carbon footprint, cloud computing, computer vision, correlation does not imply causation, crowdsourcing, digital twin, disintermediation, Douglas Hofstadter,, Erik Brynjolfsson, friendly AI, future of work, industrial robot, Internet of things, inventory management, iterative process, Jeff Bezos, job automation, job satisfaction, knowledge worker, Lyft, natural language processing, personalized medicine, precision agriculture, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, Rodney Brooks, Second Machine Age, self-driving car, sensor fusion, sentiment analysis, Shoshana Zuboff, Silicon Valley, software as a service, speech recognition, telepresence, telepresence robot, text mining, the scientific method, uber lyft

In a pilot, the company observed an 8 percent productivity improvement in logistics tasks.a At Siemens, armies of spider-styled 3-D printed robots use AI to communicate and collaborate to build things in the company’s Princeton, New Jersey, lab. Each bot is equipped with vision sensors and laser scanners. In aggregate, they join forces to manufacture on the go.b At Inertia Switch, robotic intelligence and sensor fusion enable robot-human collaboration. The manufacturing firm uses Universal Robotics’ robots, which can learn tasks on the go and can flexibly move between tasks, making them handy helpers to humans on the factory floor.c a. Dave Gershgorn, “Hitachi Hires Artificially Intelligent Bosses for Their Warehouses,” Popular Science, September 8, 2015,

“What matters is companies that don’t continue to experiment or embrace failure eventually get in the position where the only thing they can do is make a Hail Mary bet at the end of their corporate existence. I don’t believe in bet-the-company bets.”5 Instead, Bezos firmly believes in the incredible power of experimentation. (For another example of experimentation in a retail setting, see the sidebar “Controlled Chaos.”) Build-Measure-Learn The technologies that power Amazon Go—computer vision, sensor fusion, and deep learning—are systems very much under development. Limitations include cameras that have a hard time tracking loose fruits and vegetables in a customer’s hands and difficulty recognizing a customer who pulls his hat low or puts on a scarf that obscures her face. These behaviors, inadvertently or on purpose, spoofed the system during the Amazon Go test run in Seattle. But the only way to push the state of the technology forward is to explore its edges.

pages: 561 words: 163,916

The History of the Future: Oculus, Facebook, and the Revolution That Swept Virtual Reality by Blake J. Harris

4chan, airport security, Anne Wojcicki, Asian financial crisis, augmented reality, barriers to entry, Bernie Sanders, bitcoin, call centre, computer vision, cryptocurrency, disruptive innovation, Donald Trump, drone strike, Elon Musk, financial independence, game design, Grace Hopper, illegal immigration, invisible hand, Jaron Lanier, Jony Ive, Kickstarter, Marc Andreessen, Mark Zuckerberg, Menlo Park, Minecraft, move fast and break things, move fast and break things, Network effects, Oculus Rift, Peter Thiel, QR code, sensor fusion, side project, Silicon Valley, skunkworks, Skype, slashdot, Snapchat, software patent, stealth mode startup, Steve Jobs, unpaid internship, white picket fence

“When I start on something new, I really like to try and understand it deeply so I can be effective. In some cases that may slow down progress in the beginning, but that makes me feel like I can really move fast down the line and make the right choices.” “That makes sense to me.” “I have not yet worked with gyroscopes before. So the orientation tracking, it is all very new to me. But I am beginning to better understand what needs to be done; I started reading about sensor fusion.” Sensor fusion is, literally, the process of fusing together data from multiple sensors. Specifically, Antonov was referring to the need for a VR headset to track head movements and orientation in a three-dimensional space. Central to accomplishing this is something called an inertial measurement unit (IMU), which is an electronic device that detects the rate of acceleration (via accelerometers), the rate of angular velocity (via gyroscopes), and the intensity of a magnetic field (via magnetometers).

NATE MITCHELL The company was actually founded by Palmer Luckey . . . he actually designed and invented the Rift in his parents’ garage over the course of two and a half years. He was always superpassionate about head-mounted displays and virtual reality. And he wanted something that actually allowed him to jack into the matrix for video games. PALMER LUCKEY The biggest change is that we’ve developed our own motion tracker sensor chips . . . [which] gives us better data, more samples to work with when we’re doing our sensor fusion, so we can get better tracking overall; and most importantly, because it’s running at 1000 Hz (instead of 250 Hz) and it’s four times faster, we can actually have less latency. Less time between when you make a motion and when it shows up on-screen. TESTED So right now you have, with head tracking, roll . . . but you don’t have depth yet. Is that something you guys are looking forward to doing?

pages: 404 words: 95,163

Amazon: How the World’s Most Relentless Retailer Will Continue to Revolutionize Commerce by Natalie Berg, Miya Knights

3D printing, Airbnb, Amazon Web Services, augmented reality, Bernie Sanders, big-box store, business intelligence, cloud computing, Colonization of Mars, commoditize, computer vision, connected car, Donald Trump, Doomsday Clock, Elon Musk, gig economy, Internet of things, inventory management, invisible hand, Jeff Bezos, market fragmentation, new economy, pattern recognition, Ponzi scheme, pre–internet, QR code, race to the bottom, recommendation engine, remote working, sensor fusion, sharing economy, Skype, supply-chain management, TaskRabbit, trade route, underbanked, urban planning, white picket fence

No 2017 Amazon Returns Fulfilment Unique agreement with Kohl’s department stores where Amazon shoppers can return unwanted online orders to their local Kohl’s. Addresses the perennial headache that is online returns, while driving footfall to Kohl’s. We expect this to be rolled out internationally. No 2018 Amazon Go Retail First checkout-free store. Shoppers scan their Amazon app to enter. The high-tech convenience store uses a combination of computer vision, sensor fusion and deep learning to create a frictionless customer experience. No 2019 and beyond Fashion or furniture stores would be a logical next step NOTE Amazon Go officially opened its doors to the public in 2018 SOURCE Amazon; author research as of June 2018 However, it was Amazon’s rather ironic launch of physical bookstores in 2015 that marked a genuine shift in strategy, as this was the first time Amazon mimicked digital merchandising and pricing in a physical setting.

Pervasive interfaces: remove any barriers to shopping, such as technical issues that may arise with scan-as-you-shop, self-service systems that rely on customers to use their own mobile phones or purpose-built handheld devices provided by the retailer, at that retailer’s capital expense. The use of a mobile app is the most friction-free way to ensure a smooth Amazon Go experience when a customer enters the store. The removal of any human interface from the most friction-filled process of any store-based shopping journey, ie checkout, affords the customer unprecedented speed and simplicity. Autonomous computing: AI-based computer vision, sensor fusion and deep learning technologies power Amazon Go’s Just Walk Out technology. Just Walk Out technology operates without manual intervention, eliminating the need for checkout staff or hardware. It also eliminates shrinkage as a major source of loss for traditional brick and mortar retailers. Customers are charged with whatever goods they walk out with, even if they try to hide the fact from the store’s extensive computer vision camera systems.

pages: 205 words: 20,452

Data Mining in Time Series Databases by Mark Last, Abraham Kandel, Horst Bunke

call centre, computer vision, discrete time, G4S, information retrieval, iterative process, NP-complete, p-value, pattern recognition, random walk, sensor fusion, speech recognition, web application

Interesting applications of the median concept have been demonstrated in dealing with 2D shapes [16, 33], binary feature maps [23], 3D rotation [9], geometric features (points, lines, or 3D frames) [32], brain models [12], anatomical structures [37], and facial images [31]. In this paper we discuss the adaptation of the median concept to the domain of strings. The median concept is useful in various contexts. It represents a fundamental quantity in statistics. In sensor fusion, multisensory measurements of some quantity are averaged to produce the best estimate. Averaging the results of several classifiers is used in multiple classifier systems in order to achieve more reliable classifications. The outline of the chapter is as follows. We first formally introduce the median string problem in Section 2 and provide some related theoretical results in Section 3. Sections 4 and 5 are devoted to algorithmic procedures for efficiently computing set median and generalized median strings.

pages: 280 words: 74,559

Fully Automated Luxury Communism by Aaron Bastani

"Robert Solow", autonomous vehicles, banking crisis, basic income, Berlin Wall, Bernie Sanders, Bretton Woods, capital controls, cashless society, central bank independence, collapse of Lehman Brothers, computer age, computer vision, David Ricardo: comparative advantage, decarbonisation, dematerialisation, Donald Trump, double helix, Elon Musk, energy transition, Erik Brynjolfsson, financial independence, Francis Fukuyama: the end of history, future of work, G4S, housing crisis, income inequality, industrial robot, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Isaac Newton, James Watt: steam engine, Jeff Bezos, job automation, John Markoff, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, Kuiper Belt, land reform, liberal capitalism, low earth orbit, low skilled workers, M-Pesa, market fundamentalism, means of production, mobile money, more computing power than Apollo, new economy, off grid, pattern recognition, Peter H. Diamandis: Planetary Resources, post scarcity, post-work, price mechanism, price stability, private space industry, Productivity paradox, profit motive, race to the bottom, RFID, rising living standards, Second Machine Age, self-driving car, sensor fusion, shareholder value, Silicon Valley, Simon Kuznets, Slavoj Žižek, stem cell, Stewart Brand, technoutopianism, the built environment, the scientific method, The Wealth of Nations by Adam Smith, Thomas Malthus, transatlantic slave trade, Travis Kalanick, universal basic income, V2 rocket, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, working-age population

While the world economy may be much bigger now than it was in 1900, employing more people and enjoying far higher output per person, the lines of work nearly everyone performs – drivers, nurses, teachers and cashiers – aren’t particularly new. Actually Existing Automation In March 2017 Amazon launched its Amazon GO store in downtown Seattle. Using computer vision, deep learning algorithms, and sensor fusion to identify selected items the company looked to build a near fully automated store without cashiers. Here Amazon customers would be able to buy items simply by swiping in with a phone, choosing the things they wanted and swiping out to leave, their purchases automatically debited to their Amazon account. Several months later Amazon acquired Whole Foods Market for $13.7 billion. While that might have appeared a strange acquisition for a company whose core business is online retail, the purchase provided them with the supply chain capabilities to support Amazon GO and take aim at the $800 billion global grocery market.

Autonomous Driving: How the Driverless Revolution Will Change the World by Andreas Herrmann, Walter Brenner, Rupert Stadler

Airbnb, Airbus A320, augmented reality, autonomous vehicles, blockchain, call centre, carbon footprint, cleantech, computer vision, conceptual framework, connected car, crowdsourcing, cyber-physical system, DARPA: Urban Challenge, data acquisition, demand response, digital map, disruptive innovation, Elon Musk, fault tolerance, fear of failure, global supply chain, industrial cluster, intermodal, Internet of things, Jeff Bezos, Lyft, manufacturing employment, market fundamentalism, Mars Rover, Masdar, megacity, Pearl River Delta, peer-to-peer rental, precision agriculture, QWERTY keyboard, RAND corporation, ride hailing / ride sharing, self-driving car, sensor fusion, sharing economy, Silicon Valley, smart cities, smart grid, smart meter, Steve Jobs, Tesla Model S, Tim Cook: Apple, uber lyft, upwardly mobile, urban planning, Zipcar

However, it now seems clear that Google is primarily interested in collecting data on drivers and their vehicles, and that it sees the software as a new business model and does not want to produce cars. Players 183 Early in 2016, Nvidia surprisingly announced its own computing platform for controlling autonomous vehicles. This platform has sufficient processing power to support deep learning, sensor fusion and surround vision, all of which are key elements for a self-driving car. It also announced that its PX2 would be used as a standard computer in the Roborace series for self-driving race. Nvidia has also already built autonomous test vehicles, which have only been driven on test routes to date. Meanwhile, it has been licensed by the State of California Department of Motor Vehicles to use these vehicles on public roads in California.

pages: 590 words: 152,595

Army of None: Autonomous Weapons and the Future of War by Paul Scharre

active measures, Air France Flight 447, algorithmic trading, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, brain emulation, Brian Krebs, cognitive bias, computer vision, cuban missile crisis, dark matter, DARPA: Urban Challenge, DevOps, drone strike, Elon Musk,, Erik Brynjolfsson, facts on the ground, fault tolerance, Flash crash, Freestyle chess, friendly fire, IFF: identification friend or foe, ImageNet competition, Internet of things, Johann Wolfgang von Goethe, John Markoff, Kevin Kelly, Loebner Prize, loose coupling, Mark Zuckerberg, moral hazard, mutually assured destruction, Nate Silver, pattern recognition, Rodney Brooks, Rubik’s Cube, self-driving car, sensor fusion, South China Sea, speech recognition, Stanislav Petrov, Stephen Hawking, Steve Ballmer, Steve Wozniak, Stuxnet, superintelligent machines, Tesla Model S, The Signal and the Noise by Nate Silver, theory of mind, Turing test, universal basic income, Valery Gerasimov, Wall-E, William Langewiesche, Y2K, zero day

New machine learning approaches such as deep neural networks are very good at object recognition but are vulnerable to “fooling image” attacks. Without a human in the loop as a final check, using this technology to do autonomous targeting today would be exceedingly dangerous. Neural networks with these vulnerabilities could be manipulated into avoiding enemy targets and attacking false ones. In the near term, the best chances for high-reliability target recognition lie with the kind of sensor fusion that DARPA’s CODE project envisions. By fusing together data from multiple angles and multiple types of sensors, computers could possibly distinguish between military targets and civilian objects or decoys with high reliability. Objects that are dual-use for military and civilian purposes, such as trucks, would be more difficult since determining whether they are lawful targets might depend on context.

pages: 586 words: 186,548

Architects of Intelligence by Martin Ford

3D printing, agricultural Revolution, AI winter, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, barriers to entry, basic income, Baxter: Rethink Robotics, Bayesian statistics, bitcoin, business intelligence, business process, call centre, cloud computing, cognitive bias, Colonization of Mars, computer vision, correlation does not imply causation, crowdsourcing, DARPA: Urban Challenge, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, Fellow of the Royal Society, Flash crash, future of work, gig economy, Google X / Alphabet X, Gödel, Escher, Bach, Hans Rosling, ImageNet competition, income inequality, industrial robot, information retrieval, job automation, John von Neumann, Law of Accelerating Returns, life extension, Loebner Prize, Mark Zuckerberg, Mars Rover, means of production, Mitch Kapor, natural language processing, new economy, optical character recognition, pattern recognition, phenotype, Productivity paradox, Ray Kurzweil, recommendation engine, Robert Gordon, Rodney Brooks, Sam Altman, self-driving car, sensor fusion, sentiment analysis, Silicon Valley, smart cities, social intelligence, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, Ted Kaczynski, The Rise and Fall of American Growth, theory of mind, Thomas Bayes, Travis Kalanick, Turing test, universal basic income, Wall-E, Watson beat the top human players on Jeopardy!, women in the workforce, working-age population, zero-sum game, Zipcar

I found machine perception particularly fascinating: the challenges of how to build learning algorithms for distributed and multi-agent systems, how to use machine learning algorithms to make sense of environments, and how to develop algorithms that could autonomously build models of those environments, in particular, environments where you had no prior knowledge of them and had to learn as you go—like the surface of Mars. A lot of what I was working on had applications not just in machine vision, but in distributed networks and sensing and sensor fusion. We were building these neural network-based algorithms that were using a combination of Bayesian networks of the kind Judea Pearl had pioneered, Kalman filters and other estimation and prediction algorithms to essentially build machine learning systems. The idea was that these systems could learn from the environment, learn from input data from a wide range of sources of varying quality, and make predictions.