题目/Title：Hardware-Software Co-Design of Matrix-Solving for Non-Linear Optimization in SLAM Systems

作者/Author：
                        Liting Niu, Weiyi Zhang, Cheng Nian, Fei Shao, Fasih Ud Din Farrukh, Chun Zhang

会议/Conference：IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society

地点/Location：Singapore, Singapore

年份/Issue Date：2023.16-19 Oct.

页码/pages：pp.1-8

Simultaneous Localization and Mapping (SLAM) is one of the most important techniques for autonomous robots that enables the robot aware of its current position and the surrounding environment. There is a significant improvement in the accuracy with the advancement in SLAM algorithms. However, the computation complexity increases accordingly and the embedded processors of autonomous robots struggle to support heavy calculation. Matrix-solving contributes a major portion of calculation time and considering sub-tasks such as bundle adjustment takes over 40% of total time. Therefore, it is significant to optimize the calculations required for matrix-solving. However, previous works for matrix-solving accelerators are generalized and the specific matrix form in SLAM problems is not fully considered. This work concentrates on the dedicated software and hardware codesign of the matrix-solving task in SLAM systems and provides three solutions for different scales of matrix-solving problems in SLAM. The proposed FSFI-Cholesky and FI-Iterative method have achieved up to 120.2x speed improvement over the non-optimized Cholesky algorithm. Moreover, this work also reduces the execution time by more than 7.0x compared to the state-of-the-art design with fewer DSPs used for both dense and sparse matrices.