Abstract: The emerging mobile robot industry has spurred a flurry of interest in solving the simultaneous localization and mapping (SLAM) problem. However, existing SLAM platforms have difficulty in meeting the real-time and low-power requirements imposed by mobile systems. Though specialized hardware is promising with regard to achieving high performance and lowering the power, designing an efficient accelerator for SLAM is severely hindered by a wide variety of SLAM algorithms. Based on our detailed analysis of representative SLAM algorithms, we observe that SLAM algorithms advance two challenges for designing efficient hardware accelerators: the large number of computational primitives and irregular control flows. To address these two challenges, we propose a hardware accelerator that features composable computation units classified as the matrix, vector, scalar, and control units. In addition, we design a hierarchical instruction set for coping with a broad range of SLAM algorithms with irregular control flows. Experimental results show that, compared against an Intel x86 processor, on average, our accelerator with the area of 7.41 mm² achieves 10.52x and 112.62x better performance and energy savings, respectively, across different datasets. Compared against a more energy-efficient ARM Cortex processor, our accelerator still achieves 33.03x and 62.64x better performance and energy savings, respectively.