Intelligent Vehicle Localization Research Articles

A generic multi-sensor fusion scheme for localization of autonomous platforms using moving horizon estimation

In this paper, a generic multi-sensor fusion framework is developed for the localization of intelligent vehicles and mobile robots. The localization framework is based on moving horizon estimation (MHE). Unlike the commonly used probabilistic filtering algorithms – for example, extended Kalman filter (EKF) and unscented Kalman filter (UKF) – MHE relies on solving successive least squares optimization problems over the innovation of multiple sensors’ measurements and a specific estimation horizon. In this paper, we present an efficient and generic multi-sensor fusion scheme, based on MHE. The proposed multi-sensor fusion scheme is capable of operating with different sensors’ rates, missing measurements, and outliers. Moreover, the proposed scheme is based on a multi-threading architecture to reduce its computational cost, making it more feasible for practical applications. The MHE fusion method is tested using simulated data as well as real experimental data sequences from an intelligent vehicle and a mobile robot combining measurements from different sensors to get accurate localization results. The performance of MHE is compared against that of UKF, where the MHE estimation results show superior performance.

Networked Nonlinear Fusion Estimation Under DoS Attacks

This paper is concerned with the distributed fusion estimation problem for nonlinear systems under denial of service (DoS) attacks, where it is not required to know the statistical property of DoS attacks and the bounds of system noises in advance. A compensation model is constructed to defend against DoS attacks in order to reduce performance loss. Following the idea of bounded recursive optimization, two convex optimization problems, which are established in terms of linear matrix inequalities, are designed to obtain the local optimal estimator gains and the optimal weighting matrices by minimizing the upper bounds of estimation errors at each time. Finally, an intelligent vehicle localization system is used to show the validity of the results.

From Simultaneous Localization and Mapping to Collaborative Localization for Intelligent Vehicles

This paper describes a new method for real-time collaborative localization for intelligent vehicles based on a combination of an extension of simultaneous localization and mapping and track-to-track fusion methods. This strategy aims to overcome the shortcomings of the embedded proprioceptive sensors and solve the localization issues in a cluster of intelligent vehicles, which are equipped with low-cost navigation systems (global position systems, rangefinders, and vehicle to vehicle communication means) in an unknown environment. The strategy improves the vehicles’ localization accuracy and tracks a cluster of vehicles for advanced driver assistance systems and vehicles self-driving. For real-time purposes, the computational complexity of standard solutions, which are based on Kalman filter derivatives, is transformed, herein, from cubic to quadratic with respect to the state vector of the cluster. Furthermore, the correlated data is another challenge that needs to be solved in cooperative localization scenarios. To this end, two independent, local and global, fusion nodes are associated with each vehicle. The local node exploits multiple forms of the extended Kalman filter and its dual extended information filter to estimate the vehicles’ state, information vectors, covariance and information matrices in the moments and information forms, respectively. Then, the vehicles broadcast the result of their respective local fusion node to cluster. The information is used to solve the second problem, which is a track-to-track fusion on distributed architectures, with two methods: covariance intersection and information matrix fusion in the global fusion node. The simulation and the experimental results of the proposed methods, on distributed architectures, are compared to the optimal solution of centralized data fusion, and to the traditional SLAM method in terms of root mean square errors and consistency.

Visual Map-Based Localization for Intelligent Vehicles From Multi-View Site Matching

Accurate localization is a crucial step for intelligent vehicles (IVs). And vision-based localization methods are promising due to its good accuracy and low cost. However, vision-based methods are usually not robust enough due to the errors of matching similar road scenarios. In this paper, we proposed a visual map-based localization method, called multi-view site matching (MVSM). We proposed using two camera views (i.e., downward-view and front-view) to construct visual map. The visual map consists of a serial of nodes. Each node encodes the features of the road, the 2D structure, and the poses of the vehicle. Based on the constructed visual map, we proposed a multi-scale method for accurate vehicle localization. In coarse localization, we adopt a topological model to obtain a set of candidate nodes from visual map. Furthermore, holistic features from front view are matched within the candidates such that the best matched node is determined for image-level localization. In metric localization, the best matched is first verified with the local features from downward view. And the vehicle pose is finally computed by utilizing the 2D structure from the verified nodes in the map. In the experiment, the proposed MVSM method has been tested with actual field data covering different pavement types in different seasons. The proposed MVSM method can achieve less than 0.20m mean localization errors. Compared to existing vision-based methods, the proposed method utilizes two views to enhance image-level localization and 2D pavement structure to improve metric localization so as to greatly improve the overall localization performance.

A Fusion Method for Localization of Intelligent Vehicles in Carparks

With the increasing demand for urban space, more and more multistory carparks are needed as they will play a central role in the city transportation system. An autonomous navigation solution for Intelligent Vehicles in these indoor scenarios is then required. One step to solve this problem is to localize Intelligent Vehicles in these specific environments. However, the lack of GPS due to signal obstruction (multipath, non-line of sight, interference, etc.) appears to be a significant concern for any localization system, let alone indoor ones. Hence, in this paper, a wireless sensor network based approach is proposed to replace the GPS (Global Positioning System) role for indoor environments. In addition, a fusion framework for multiple sensors such as Wi-Fi access points, Inertial Measurement Unit (IMU) or LiDAR is studied. Experiments in almost one-year duration yield a stable result of mean global localization error at 0.5m.

A Novel Online Approach for Drift Covariance Estimation of Odometries Used in Intelligent Vehicle Localization.

Localization is the fundamental problem of intelligent vehicles. For a vehicle to autonomously operate, it first needs to locate itself in the environment. A lot of different odometries (visual, inertial, wheel encoders) have been introduced through the past few years for autonomous vehicle localization. However, such odometries suffers from drift due to their reliance on integration of sensor measurements. In this paper, the drift error in an odometry is modeled and a Drift Covariance Estimation (DCE) algorithm is introduced. The DCE algorithm estimates the covariance of an odometry using the readings of another on-board sensor which does not suffer from drift. To validate the proposed algorithm, several real-world experiments in different conditions as well as sequences from Oxford RobotCar Dataset and EU long-term driving dataset are used. The effect of the covariance estimation on three different fusion-based localization algorithms (EKF, UKF and EH-infinity) is studied in comparison with the use of constant covariance, which were calculated based on the true variance of the sensors being used. The obtained results show the efficacy of the estimation algorithm compared to constant covariances in terms of improving the accuracy of localization.

Multi-View and Multi-Scale Localization for Intelligent Vehicles in Underground Parking Lots

Because of limited access to global positioning system (GPS) signals, accurate and reliable localization for intelligent vehicles in underground parking lots is still an open problem. This paper proposes a multi-view and multi-scale localization method aiming at solving this problem. The proposed method is divided into an offline mapping stage and an online localization stage. In the mapping stage, the offline map is generated by fusing 3-D information, WiFi features, visual features, and trajectory from visual odometry (VO). In the localization stage, WiFi fingerprint matching is exploited for coarse localization. Based on the result of coarse localization, multi-view localization is exploited for image-level localization. Finally, metric localization is exploited to refine the localization results. By applying this multi-scale strategy, it is possible to fuse WiFi localization and visual localization and reduce the image matching and error rate to a great extent. Because of exploiting more information, multi-view localization is more robust and accurate than single-view localization. The method is tested in a 2,000 m2 underground parking lot. The result demonstrates that this method can achieve sub-meter localization on average. The proposed localization method can be a supplement to the existing intelligent vehicle localization techniques.

Rigid Point Set Registration Based on Cubature Kalman Filter and Its Application in Intelligent Vehicles

Point set registration is a key problem in intelligent vehicle localization and mapping. This paper presents a rigid point set registration algorithm based on the cubature Kalman filter (CKF). First, the point set registration problem is cast into the state space model assuming that the correspondence between these two point sets is previously unknown. Then, CKF is used to solve this nonlinear filtering problem. At every iterative step, all the points in the moving point set will be considered, and the corresponding points in the model point set will be updated accordingly. In the time update, the scale of the free space that can be explored is significant for this registration algorithm. Herein, continuous simulated annealing (CSA) is adopted to gradually optimize the covariance of the model noise to speed up convergence. Tests on public data sets show that the CKF-based point set registration algorithm is robust to outliers, noise, and initialization misalignment. Then, the application of this algorithm on intelligent vehicles and intelligent transportation systems is demonstrated through mapping and localization experiments. The precision and robustness are both validated compared with the traditional ICP, NDT, and CPD based point set registrations in the localization experiments. Thus, the contributions of this paper are threefold. First, a CKF scheme is utilized in the point set registration problem. Second, CSA serves as the local optimizer to speed up the convergence process and make it more accurate. Third, this filtering-based method is adopted in intelligent vehicles and SLAM applications.

Image Sequence Matching Using Both Holistic and Local Features for Loop Closure Detection

Simultaneous localization and mapping (SLAM) has a wide range of applications, such as mobile robots, intelligent vehicle localization, and intelligent transportation system. However, loop closure detection is a challenge task for SLAM. This task concerns the difficulty of recognizing already mapped areas. To this end, this paper proposes a novel loop closure detection method called image sequence matching (ISM), which only uses a low-cost monocular camera. This method first divides the already mapped areas into some “feature-zones.” One feature-zone is selected by a novel topological detection model. Then, we adopt two different feature spaces to make sequence matching between query image and feature-zone. Last but not least, we propose a novel clustering method called voting K-nearest neighbor to fuse candidates. As a result, the ISM method has been validated by using collection data sets and public data sets, which were collected along different routes, covering different times and weather conditions. The total lengths of these routes are more than 10 km. Experimental results show that the ISM method can adapt to different times with good detection stability in varying scenarios. The mean of detection errors is all less than 1 frame and the detection accuracies are all more than 90% in these scenarios. Compared with other methods, the proposed method has high accuracy and great robustness.

Hierarchical Neighborhood Based Precise Localization for Intelligent Vehicles in Urban Environments

High-precision localization has drawn more and more attention in recent research of intelligent vehicle systems and autonomous robot navigation technology. In most methods, the approaches are only effective in some specific situations. In other words, these methods can only perform well with obvious features, like tall building walls, road curbs, etc . In this paper, a novel framework for precise localization of autonomous vehicle applying to different scenes especially some typical urban environments is proposed. The main procedures of this method include mapping and localization. During mapping process, inertial measurement unit, odometry, and high-precision GPS are fused together with the data sensed by LIDAR, a high-precision map that could provide global position and pose is generated using rolling window. When localizing, live laser data align with the prior-map. A particle filter based point cloud matching method is utilized here. Based on this, a hierarchical localizing method is proposed, which is more accurate and faster than the original matching method. With this method, the sampling guided by proposal density is propagated upward every hierarchy. Besides that, some accelerating algorithms are utilized to make this approach real time. Finally, decimeter-level localization is achieved in different environments, which is proven by some experiments.

Stereo Vision Based Localization Method for Intelligent Vehicle

In order to overcome the accumulated error in traditional localization methods for intelligent vehicle such as dead reckoning and visual odometry, a simultaneous localization and mapping(SLAM) algorithm based on stereo vision was presented in the paper. Firstly, the interrelated elements in the localization method were defined, and the probability model for intelligent vehicle localization was proposed, then the motion and observation model were established, and the detailed implementation of the introduced localization algorithm was given. Finally, an experiment was designed to confirm the effectiveness of the proposed method. Experimental results indicate that the algorithm can realize three-dimensional motion estimation of intelligent vehicle and can improve the positioning precision effectively.

A Distributed Algorithm Based on RSSI for Intelligent Vehicle Localization

Wireless sensor network is added on traditional GPS to realize double location in this paper. The widely used distributed distance measure algorithm of RSSI based in wireless sensor network is adopted. At the beginning work of this position system, unknown position node A broadcast its localization request, after all the base node received this request, they reply node A with its own location info and the RSSI value from other base node. Node A select the most closest base node as base to determine his own location, moreover, distance between base node and signal strength is take as reference to adjust the weight value of every base node, then the precise position is got after final calculation. In the end, precise of this algorithm is proved by simulation experiment.

Improved Intelligent Vehicle Localization Using Magnetic Ruler

Improved Intelligent Vehicle Localization Using Magnetic Ruler

Intelligent Vehicle Localization in Urban Environments Using EKF-based Visual Odometry and GPS Fusion

AbstractWe present a method for intelligent vehicle localization and 3D mapping in urban environments using integration of stereovision and Real-Time Kinematic GPS (RTK-GPS). In our approach, stereoscopic system is used to recover the scene geometry and to predict the camera motion. Accurate GPS positions are integrated to correct the vehicle positions and orientations using Extended Kalman Filter (EKF), and to rectify the global positions of reconstructed 3D landmarks based on the positions of reference stereo frames. The method was tested using data obtained by a real electrical GEM vehicle equipped with stereoscopic system and RTK-GPS in urban environments. The results show that the trajectory obtained by GPS/Vision integrated method can fit the ground truth better than the vision-only method, and can also avoid the position jumps aroused by GPS signal failures.

Improved Intelligent Vehicle Localization Using Magnetic Ruler

This paper proposes an improved intelligent vehicle localization method, which integrates the data from the odometry and the magnetic ruler(MR). The MR is used to detect the magnetic markers, which provides the absolute positioning reference. The data from odometry and the MR are used to localize the vehicle by using unscented Kalman filter. The data association (DA) method is adopted to validate the observations from the MR. The experiment results show that the method is a robust way.

Ground-Texture-Based Localization for Intelligent Vehicles

Localization is a critical problem in the research of intelligent vehicles. Although it can be achieved by using a real-time kinematic global positioning system (RTK-GPS, or fused with other methods such as dead reckoning), it may be unfeasible if every vehicle has to be equipped with such an expensive sensor. This paper proposes a ground-texture-based map-matching approach to address the localization problem. To reduce the effect of complicated illumination in outdoor environments, a camera is fixed downward at the bottom of a vehicle, and controllable lights are also equipped around the camera for consistent illumination. The proposed approach includes two steps: 1) mapping and 2) localization. RTK-GPS is only used in the mapping, and other sensor data from camera and odometry are captured with time stamps to create a global ground texture map. A multiple-view registration-based optimization algorithm is applied to improve map accuracy. In the localization step, vehicle pose is estimated by matching the current camera frame with the best submap frame and by fusion strategy. Results with both synthetic and real experiments prove the feasibility and effectiveness of the proposed approach.