使用Berkeley Aachen机器人工具包（BART）快速集成和校准新传感器
Rapid Integration and Calibration of New Sensors Using the Berkeley Aachen Robotics Toolkit (BART)
摘要：
在三次DARPA大挑战赛之后，世界各地的许多团体继续积极研究和致力于能够在给定环境（例如沙漠，城市）中完成任务的自动驾驶车辆，同时遵循一套规定的规则，但没有一个在不受控制的环境中完全成功，许多人每天都在完成一项任务。我们认为，除了改进汽车中使用的传感器和用于处理信息的人工智能算法外，社区还应该关注问题的系统工程方面，即汽车的局限性（在空间、功率或散热方面）和软件开发周期的局限性。本文探讨了这些问题和我们克服这些问题的经验。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
