Reliability is a key factor for realizing safety guarantee of full autonomous robot systems. In this paper, we focus on reliability in mobile robot localization. Monte Carlo localization (MCL) is widely used for mobile robot localization. However, it is still difficult to guarantee its safety because there are no methods determining reliability for MCL estimate. This paper presents a novel localization framework that enables robust localization, reliability estimation, and quick re-localization, simultaneously. The presented method can be implemented using similar estimation manner to that of MCL. The method can increase localization robustness to environment changes by estimating known and unknown obstacles while performing localization; however, localization failure of course occurs by unanticipated errors. The method also includes a reliability estimation function that enables us to know whether localization has failed. Additionally, the method can seamlessly integrate a global localization method via importance sampling. Consequently, quick re-localization from failures can be realized while mitigating noisy influence of global localization. Through three types of experiments, we show that reliable MCL that performs robust localization, self-failure detection, and quick failure recovery can be realized.

Google co-founder Larry Page's flying car startup Kittyhawk is winding down, the company announced Wednesday. "We're still working on the details of what's next," the company wrote in a LinkedIn post. Kittyhawk was founded as Zee.Aero in 2010 when Page recruited Sebastian Thrun, who had worked on self-driving cars and other experimental projects at Google, to create electric vertical takeoff and landing aircraft. The company unveiled a demonstration video of a flying car in 2017, and Thrun said he envisioned a time when people would be able to hail flying cars through an app like Lyft or Uber. Kittyhawk showcased a flying car model called the Flyer in 2018 that could hold one person and fly up to 20 miles. Thrun told CNBC in an interview earlier that year that the models could take to the skies within five years.

Kitty Hawk might have the backing of Google's Larry Page, but that doesn't mean things are going smoothly. Forbes has learned that Kitty Hawk dropped key engineer Damon Vander Lind in May after "months" of fighting with Page and CEO Sebastian Thrun over the company's strategy. Page and Thrun want to build a larger version of the Heaviside air taxi that autonomously carries two passengers with a remote pilot as backup, but Vander Lind reportedly felt this was "too risky." There were also accusations that Vander Lind was unreceptive to ideas and at times hostile to staff, Forbes sources claimed. Kitty Hawk further dealt with separate complaints of sexism.

Algorithms implementing populations of agents which interact with one another and sense their environment may exhibit emergent behavior such as self-organization and swarm intelligence. Here a swarm system, called Databionic swarm (DBS), is introduced which is able to adapt itself to structures of high-dimensional data characterized by distance and/or density-based structures in the data space. By exploiting the interrelations of swarm intelligence, self-organization and emergence, DBS serves as an alternative approach to the optimization of a global objective function in the task of clustering. The swarm omits the usage of a global objective function and is parameter-free because it searches for the Nash equilibrium during its annealing process. To our knowledge, DBS is the first swarm combining these approaches. Its clustering can outperform common clustering methods such as K-means, PAM, single linkage, spectral clustering, model-based clustering, and Ward, if no prior knowledge about the data is available. A central problem in clustering is the correct estimation of the number of clusters. This is addressed by a DBS visualization called topographic map which allows assessing the number of clusters. It is known that all clustering algorithms construct clusters, irrespective of the data set contains clusters or not. In contrast to most other clustering algorithms, the topographic map identifies, that clustering of the data is meaningless if the data contains no (natural) clusters. The performance of DBS is demonstrated on a set of benchmark data, which are constructed to pose difficult clustering problems and in two real-world applications.

The localization of self-driving cars is needed for several tasks such as keeping maps updated, tracking objects, and planning. Localization algorithms often take advantage of maps for estimating the car pose. Since maintaining and using several maps is computationally expensive, it is important to analyze which type of map is more adequate for each application. In this work, we provide data for such analysis by comparing the accuracy of a particle filter localization when using occupancy, reflectivity, color, or semantic grid maps. To the best of our knowledge, such evaluation is missing in the literature. For building semantic and colour grid maps, point clouds from a Light Detection and Ranging (LiDAR) sensor are fused with images captured by a front-facing camera. Semantic information is extracted from images with a deep neural network. Experiments are performed in varied environments, under diverse conditions of illumination and traffic. Results show that occupancy grid maps lead to more accurate localization, followed by reflectivity grid maps. In most scenarios, the localization with semantic grid maps kept the position tracking without catastrophic losses, but with errors from 2 to 3 times bigger than the previous. Colour grid maps led to inaccurate and unstable localization even using a robust metric, the entropy correlation coefficient, for comparing online data and the map.

The application of artificial intelligence (AI) and machine learning to the business and IT, from intelligent IT operations (AIOps) to service management to software testing, is keeping the data revolution moving at lightning speed. That's why data science remains a popular concentration for computer science students who have the talent for math and analytics. And it's why more organizations are clamoring for data scientists who can help make decisions faster and put their businesses ahead of competitors. In today's age data science expertise with desirable knowledge in relatable fields is rare to find and therefore we have enlisted top 10 data science experts who you can follow in Twitter. Hilary is the Founder of Fast Forward Labs, a machine intelligence research company, and the Data Scientist in Residence at Accel.

As Silicon Valley's entrepreneurs cluster around the worldview that artificial intelligence is poised to change how we work, investors are deciding which use cases make the most sense to pump money into right now. One focus has been the relentless communication between companies and customer that takes place at call centers. Call center tech has spawned dozens if not hundreds of AI startups, many of which have focused on automating services and using robotic voices to point customers somewhere they can spend money. There has been a lot of progress, but not all of those products have delivered. Cresta is more focused on using AI suggestions to help human contact center workers make the most of an individual call or chat session and lean on what's worked well for past interactions that were deemed successful.

In March 2018, an eerie portrait created by an artificial intelligence program sold at Christie's Auction House for almost half a million dollars. A few months later, a movie written and directed by an AI algorithm was released amid much hype. And this March, a record company signed an AI artist for the first time. Artificial creativity is the subject of the second episode of the Sleepwalkers podcast, an ongoing series exploring the implications of AI. Machine-made art has flourished in recent years, thanks to advances in AI, and some examples are both impressive and unnerving.

Once you get up high enough, you don't have to worry about a lot of the obstacles like pedestrians and traffic jams that plague autonomous cars. That's why Sebastian Thrun, Google's self-driving team founder turned CEO of flying vehicle startup Kitty Hawk, said onstage at TechCrunch Disrupt SF today that we should expect true autonomy to succeed in the air before the road. "I believe we're going to be done with self-flying vehicles before we're done with self-driving cars," Thrun told TechCrunch reporter Kirsten Korosec. Why? "If you go a bit higher in the air then all the difficulties with not hitting stuff like children and bicycles and cars and so on just vanishes . . . Go above the buildings, go above the trees, like go where the helicopters are!" Thrun explained, but noted personal helicopters are so noisy they're being banned in some places like Napa, Calif. That proclamation has wide-reaching implications for how cities are planned and real estate is bought.

On the day I met Sebastian Thrun in Palo Alto, the State of California legalized self-driving cars. Gov. Jerry Brown arrived at the Google campus in one of the company's computer-controlled Priuses to sign the bill into law. "California is a big deal," said Thrun, the founder of Google's autonomous-car program, "because it tends to be hard to legislate here." He said it with typical understatement. An idea that was in its technological infancy a decade ago, when Thrun and his colleagues were racing to develop a vehicle that could drive itself more than a few miles on a desert test course, was now being officially sanctioned by the country's most populous state.