# Map-Matching-Based Localization Using Camera and Low-Cost GPS for Lane-Level Accuracy +
## 2 Related Works  
The current localization approaches for autonomous driving can be classified into three types namely LiDAR-based, Camera-based, and Sensor fusion-based approaches. Vehicle localization using a probabilistic map and LiDAR has been proposed in [16]. In this work, there are tree phases used for generating the map: they firstly align the overlapping returned laser beam areas, and then perform the calibration to obtain the similar response curves, and finally make a projection of the aligned calibrated trajectories into a high-resolution probabilistic map. Another LiDAR-based map-matching technique was proposed in [17]. The authors utilized the laser sensor Velodyne HDL 32-E and proposed a localization system based on particle filter. Two map-matching distance techniques have been investigated: a modified Likelihood Field distance and an adaptive standard Cosine distance. A map-matching distance between global maps and compact global map has been used to update the poses of the particles. The global map is generated offline using car's odometry, GPS/IMU, and 3D LiDAR data and processed by GraphSLAM method. Meanwhile, the local maps are created online by employing the occupancy grid-mapping algorithm. In [18], the authors also used a Velodyne HDL-32E LIDAR and combined it with an IMU sensor to get more detailed environmental features. In this work, a two-layer LiDAR has been applied. The bottom layer contains of ground curb features, and the upper layer composed of a 2D point cloud of the vertical features. They used a combination of an a-priory map and Monte Carlo Localization (MCL) method to obtain the estimated of the vehicle's position. Compared to LiDAR, camera-based localization is a low-cost solution [19; 20]. Stereo vision-based localization has been proposed in [21], using particle filter to have a system that can locate and navigate the vehicle on the region with the absence of lane line marking. In their work, the authors extracted lane lines using classical image processing method. They firstly preprocessed the image by applying the Gabor filter followed by the Gaussian filter in y-direction to get a gradient image. Then, based on the histogram data, the image is converted into a binary image. After that, the method fits the line marking by applying RANSAC [22] algorithm. Using the stereo cameras, the method was able to estimate the width of lane and the relative distance between the vehicle and the lane lines. Finally, the authors used particle filter to optimize these parameters. There are at least two main drawbacks of this method. Firstly, since the method uses old-fashioned line detection method, the accuracy is not optimal compared to the new line detection methods based on deep learning such as [23] and LaneNet [24] (that we exploit in this work). Secondly, the method only enables navigation in local areas because it does not use a map as a guidance when performing the navigation. Under these conditions, the vehicle can experience serious positioning errors when it comes to the global navigation especially for an autonomous vehicle. The vehicle completely blinds about what to happen in the next few seconds or minutes. The vehicle does not have enough information that can tell where to look and what actions to expect. In [25], the authors developed video-based localization technique using point feature-based localization (PFL) and lane feature-based localization (LFL) methods to support the existing GNSS. These methods are complementing each other. PFL performs better in inner city scenarios whereas LFL is good in rural areas. Since this work is using 3D point based as features reference for map matching, it requires a lot of buffer memories to store the salient features. Such a system burdens the computational process that challenges the real-time performance. In addition to that, this system fuses two methods, the PFL and LFL.