3,008 research outputs found
Towards Visual Ego-motion Learning in Robots
Many model-based Visual Odometry (VO) algorithms have been proposed in the
past decade, often restricted to the type of camera optics, or the underlying
motion manifold observed. We envision robots to be able to learn and perform
these tasks, in a minimally supervised setting, as they gain more experience.
To this end, we propose a fully trainable solution to visual ego-motion
estimation for varied camera optics. We propose a visual ego-motion learning
architecture that maps observed optical flow vectors to an ego-motion density
estimate via a Mixture Density Network (MDN). By modeling the architecture as a
Conditional Variational Autoencoder (C-VAE), our model is able to provide
introspective reasoning and prediction for ego-motion induced scene-flow.
Additionally, our proposed model is especially amenable to bootstrapped
ego-motion learning in robots where the supervision in ego-motion estimation
for a particular camera sensor can be obtained from standard navigation-based
sensor fusion strategies (GPS/INS and wheel-odometry fusion). Through
experiments, we show the utility of our proposed approach in enabling the
concept of self-supervised learning for visual ego-motion estimation in
autonomous robots.Comment: Conference paper; Submitted to IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS) 2017, Vancouver CA; 8 pages, 8 figures,
2 table
Data-Centric Evolution in Autonomous Driving: A Comprehensive Survey of Big Data System, Data Mining, and Closed-Loop Technologies
The aspiration of the next generation's autonomous driving (AD) technology
relies on the dedicated integration and interaction among intelligent
perception, prediction, planning, and low-level control. There has been a huge
bottleneck regarding the upper bound of autonomous driving algorithm
performance, a consensus from academia and industry believes that the key to
surmount the bottleneck lies in data-centric autonomous driving technology.
Recent advancement in AD simulation, closed-loop model training, and AD big
data engine have gained some valuable experience. However, there is a lack of
systematic knowledge and deep understanding regarding how to build efficient
data-centric AD technology for AD algorithm self-evolution and better AD big
data accumulation. To fill in the identified research gaps, this article will
closely focus on reviewing the state-of-the-art data-driven autonomous driving
technologies, with an emphasis on the comprehensive taxonomy of autonomous
driving datasets characterized by milestone generations, key features, data
acquisition settings, etc. Furthermore, we provide a systematic review of the
existing benchmark closed-loop AD big data pipelines from the industrial
frontier, including the procedure of closed-loop frameworks, key technologies,
and empirical studies. Finally, the future directions, potential applications,
limitations and concerns are discussed to arouse efforts from both academia and
industry for promoting the further development of autonomous driving. The
project repository is available at:
https://github.com/LincanLi98/Awesome-Data-Centric-Autonomous-Driving
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