VA-LOAM: Visual Assist LiDAR Odometry and Mapping for Accurate Autonomous Navigation.

In this study, we enhanced odometry performance by integrating vision sensors with LiDAR sensors, which exhibit contrasting characteristics. Vision sensors provide extensive environmental information but are limited in precise distance measurement, whereas LiDAR offers high accuracy in distance metrics but lacks detailed environmental data. By utilizing data from vision sensors, this research compensates for the inadequate descriptors of LiDAR sensors, thereby improving LiDAR feature matching performance. Traditional fusion methods, which rely on extracting depth from image features, depend heavily on vision sensors and are vulnerable under challenging conditions such as rain, darkness, or light reflection. Utilizing vision sensors as primary sensors under such conditions can lead to significant mapping errors and, in the worst cases, system divergence. Conversely, our approach uses LiDAR as the primary sensor, mitigating the shortcomings of previous methods and enabling vision sensors to support LiDAR-based mapping. This maintains LiDAR Odometry performance even in environments where vision sensors are compromised, thus enhancing performance with the support of vision sensors. We adopted five prominent algorithms from the latest LiDAR SLAM open-source projects and conducted experiments on the KITTI odometry dataset. This research proposes a novel approach by integrating a vision support module into the top three LiDAR SLAM methods, thereby improving performance. By making the source code of VA-LOAM publicly available, this work enhances the accessibility of the technology, fostering reproducibility and transparency within the research community.

Free-Resolution Probability Distributions Map-Based Precise Vehicle Localization in Urban Areas.

We propose a free-resolution probability distributions map (FRPDM) and an FRPDM-based precise vehicle localization method using 3D light detection and ranging (LIDAR). An FRPDM is generated by Gaussian mixture modeling, based on road markings and vertical structure point cloud. Unlike single resolution or multi-resolution probability distribution maps, in the case of the FRPDM, the resolution is not fixed and the object can be represented by various sizes of probability distributions. Thus, the shape of the object can be represented efficiently. Therefore, the map size is very small (61 KB/km) because the object is effectively represented by a small number of probability distributions. Based on the generated FRPDM, point-to-probability distribution scan matching and feature-point matching were performed to obtain the measurements, and the position and heading of the vehicle were derived using an extended Kalman filter-based navigation filter. The experimental area is the Gangnam area of Seoul, South Korea, which has many buildings around the road. The root mean square (RMS) position errors for the lateral and longitudinal directions were 0.057 m and 0.178 m, respectively, and the RMS heading error was 0.281°.

Extended Line Map-Based Precise Vehicle Localization Using 3D LIDAR.

An Extended Line Map (ELM)-based precise vehicle localization method is proposed in this paper, and is implemented using 3D Light Detection and Ranging (LIDAR). A binary occupancy grid map in which grids for road marking or vertical structures have a value of 1 and the rest have a value of 0 was created using the reflectivity and distance data of the 3D LIDAR. From the map, lines were detected using a Hough transform. After the detected lines were converted into the node and link forms, they were stored as a map. This map is called an extended line map, of which data size is extremely small (134 KB/km). The ELM-based localization is performed through correlation matching. The ELM is converted back into an occupancy grid map and matched to the map generated using the current 3D LIDAR. In this instance, a Fast Fourier Transform (FFT) was applied as the correlation matching method, and the matching time was approximately 78 ms (based on MATLAB). The experiment was carried out in the Gangnam area of Seoul, South Korea. The traveling distance was approximately 4.2 km, and the maximum traveling speed was approximately 80 km/h. As a result of localization, the root mean square (RMS) position errors for the lateral and longitudinal directions were 0.136 m and 0.223 m, respectively.

Performance Enhancement of Land Vehicle Positioning Using Multiple GPS Receivers in an Urban Area.

The Global Positioning System (GPS) is the most widely used navigation system in land vehicle applications. In urban areas, the GPS suffers from insufficient signal strength, multipath propagation and non-line-of-sight (NLOS) errors, so it thus becomes difficult to obtain accurate and reliable position information. In this paper, an integration algorithm for multiple receivers is proposed to enhance the positioning performance of GPS for land vehicles in urban areas. The pseudoranges of multiple receivers are integrated based on a tightly coupled approach, and erroneous measurements are detected by testing the closeness of the pseudoranges. In order to fairly compare the pseudoranges, GPS errors and terms arising due to the differences between the positions of the receivers need to be compensated. The double-difference technique is used to eliminate GPS errors in the pseudoranges, and the geometrical distance is corrected by projecting the baseline vector between pairs of receivers. In order to test and analyze the proposed algorithm, an experiment involving live data was performed. The positioning performance of the algorithm was compared with that of the receiver autonomous integrity monitoring (RAIM)-based integration algorithm for multiple receivers. The test results showed that the proposed algorithm yields more accurate position information in urban areas.

Vertical Corner Feature Based Precise Vehicle Localization Using 3D LIDAR in Urban Area.

Tall buildings are concentrated in urban areas. The outer walls of buildings are vertically erected to the ground and almost flat. Therefore, the vertical corners that meet the vertical planes are present everywhere in urban areas. These corners act as convenient landmarks, which can be extracted by using the light detection and ranging (LIDAR) sensor. A vertical corner feature based precise vehicle localization method is proposed in this paper and implemented using 3D LIDAR (Velodyne HDL-32E). The vehicle motion is predicted by accumulating the pose increment output from the iterative closest point (ICP) algorithm based on the geometric relations between the scan data of the 3D LIDAR. The vertical corner is extracted using the proposed corner extraction method. The vehicle position is then corrected by matching the prebuilt corner map with the extracted corner. The experiment was carried out in the Gangnam area of Seoul, South Korea. In the experimental results, the maximum horizontal position error is about 0.46 m and the 2D Root Mean Square (RMS) horizontal error is about 0.138 m.

GPS/DR Error Estimation for Autonomous Vehicle Localization.

Autonomous vehicles require highly reliable navigation capabilities. For example, a lane-following method cannot be applied in an intersection without lanes, and since typical lane detection is performed using a straight-line model, errors can occur when the lateral distance is estimated in curved sections due to a model mismatch. Therefore, this paper proposes a localization method that uses GPS/DR error estimation based on a lane detection method with curved lane models, stop line detection, and curve matching in order to improve the performance during waypoint following procedures. The advantage of using the proposed method is that position information can be provided for autonomous driving through intersections, in sections with sharp curves, and in curved sections following a straight section. The proposed method was applied in autonomous vehicles at an experimental site to evaluate its performance, and the results indicate that the positioning achieved accuracy at the sub-meter level.