# Detecting and Mitigating Attacks on GPS Devices
## 2 Existing Surveys
They categorize vSLAM systems into three types based on how they use information from images: (1) direct or dense methods, (2) feature-based methods, and (3) semantic scene understanding methods. Each of these methods is described in detail, and various approaches are presented and discussed. The authors of [19] presented a brief survey of vSLAM methods, focusing on monocular techniques and presenting a qualitative and quantitative performance comparison of three of the most prominent algorithms in the literature: ORB-SLAM2 (sparse feature-based method), LSD-SLAM (semi-dense direct method), and DSO (sparse direct method). The algorithms demonstrated good performance; however, monocular approaches are susceptible to lens flare, overexposed images, water reflections, and moving objects and should be supported by other sensors, such as an IMU. Many reviews discussed individual vSLAM [18] or Lidar-SLAM [11] methods; however, they focused on multi-sensor fusion methods, where multiple orthogonal sensor outputs were fused to estimate position information. The authors of [22] reviewed vSLAM and LiDAR-SLAM approaches combined with IMU-based methods. The multi-sensor fusion techniques discussed include visual-inertial approaches, LiDAR-inertial approaches, visual-LiDAR approaches, and LiDAR-visual-inertial approaches. The authors identified several critical future research directions, including the need for a versatile and efficient sensor fusion framework, additional DL-aided methods, and distributed cooperative methods. The authors of [23] surveyed multi-sensor fusion methods combining vSLAM with IMU sensors and proposed a modular multi-sensor data fusion technique. The authors of [20] considered LiDAR SLAM and vSLAM, along with multi-sensor methods such as LiDAR-vSLAM, visual-inertial SLAM (VI-SLAM), and LiDAR-inertial SLAM. These SLAM methods were considered for different sensor types, such as monocular vs. stereo. The authors of [10] provided a comprehensive review of visual and LiDAR-based SLAM techniques and approaches, considering map and mapless approaches that perform tasks such as obstacle avoidance, path planning, and map generation. They discussed some methods that also incorporate IMU-based measurements together with visual and LiDAR methods. The authors of [24] reviewed LiDAR-based SLAM techniques, considering both 2D and 3D LiDAR-SLAM approaches, and described the technical challenges of 3D LiDAR-SLAM,specifically the typical low vertical resolution and sparse point clouds associated with LiDAR. They concluded that multi-sensor fusion methods are required. The general benefits of multi-sensor fusion approaches were discussed, focusing on inertial-aided LiDAR-SLAM and visual-aided LiDAR-SLAM. Several reviews focused on using Artificial Intelligence (AI) and machine learning (ML) in vSLAM and Lidar-SLAM. The authors of [25] provided a brief survey of vSLAM with a focus on deep learning (DL)-based methods, pointing out four key advantages of DL-based approaches to vSLAM compared with traditional approaches, which they broadly categorized as feature point methods or direct methods. First, DL methods had good invariance to illumination changes compared with traditional approaches. Second, DL-based vSLAM approaches could better identify moving objects in images than traditional approaches. Third, high-level semantic information could be extracted through DL to provide better context and understanding in map creation. Last, DL-based approaches removed the need for hand-constructed feature generation, widely understood as a general strength of DL approaches in any application. The authors discussed many of the DL approaches that researchers have already incorporated into the overall visual SLAM system, including the VO, the closed-loop detection process, and the semantic SLAM module, highlighting the performance benefits of these methods in dynamic environments. The authors of [26] also focused on AI-based approaches for UAS navigation.