SEROSep 8, 2014

Rapid Integration and Calibration of New Sensors Using the Berkeley Aachen Robotics Toolkit (BART)

arXiv:1409.2373v11 citations
Originality Synthesis-oriented
AI Analysis

It tackles practical integration problems for autonomous vehicle researchers, but is incremental as it builds on existing toolkit approaches.

The paper addresses the systems engineering challenges in autonomous vehicles, such as hardware limitations and software development cycles, and presents experiences in overcoming these issues using the Berkeley Aachen Robotics Toolkit (BART).

After the three DARPA Grand Challenge contests many groups around the world have continued to actively research and work toward an autonomous vehicle capable of accomplishing a mission in a given context (e.g. desert, city) while following a set of prescribed rules, but none has been completely successful in uncontrolled environments, a task that many people trivially fulfill every day. We believe that, together with improving the sensors used in cars and the artificial intelligence algorithms used to process the information, the community should focus on the systems engineering aspects of the problem, i.e. the limitations of the car (in terms of space, power, or heat dissipation) and the limitations of the software development cycle. This paper explores these issues and our experiences overcoming them.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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