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The bundle adjustment( BA) is a key technology for the back-end optimization of simultaneous positioning and map construction( SLAM) . Using BA online can meet real-time requirements, which is a key factor in applying it to real-time systems such as autonomous driving vehicles. This paper firstly analyzed the characteristics of SLAM data in real scene and proposed a method using sliding window to reduce the calculation scale. Then it featured the parallelism of the algorithm by analyzing the properties of sparse matrices in local BA. Finally, it implemented and accelerated the algorithm based on the embedded GPU. Applied and tested into a vehicle-mounted SLAM system in a real scene, the experimental results show that it has 4.8 times higher processing performance than the CPU on the same platform on average on the AGX Xavier with embedded GPU for720P images from road scenes. It can also handle position, pose and map data at 15 fps, which meets the real-time requirements of the vehicle-mounted system. [ABSTRACT FROM AUTHOR]

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