Simultaneous Localization and Mapping (SLAM) has always been a hot topic in the fields of intelligent industry and mobile robotics. To effectively limit drift caused by large-scale operation, we can introduce sensors that offer absolute measurements, such as GNSS. However, GNSS will have no signal, resulting in large positioning errors, due to challenging environments such as occlusion and shielding. Thus, we proposed a landmark-based multi-sensor fusion SLAM algorithm to solve the question of carrier’s location in GNSS-denied. During GNSS-denied, we use relative information between landmarks and carrier to constraint the poses of carrier, thereby effectively eliminating pose drift and achieving high-precision autonomous positioning. Additionally, we consider three elements, landmarks distribution, the number of landmarks and the style of relative information, may influence the performance of proposed algorithm, we conducted multiple experiments in two-dimensional (2-D) and three-dimensional (3-D) spaces to verify its impact on pose estimation.