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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (

To reduce the data size of metric map and map matching computational cost in unmanned ground vehicle self-driving navigation in urban scenarios, a metric-topological hybrid map navigation system is proposed in this paper. According to the different positioning accuracy requirements, urban areas are divided into strong constraint (SC) areas, such as roads with lanes, and loose constraint (LC) areas, such as intersections and open areas. As direction of the self-driving vehicle is provided by traffic lanes and global waypoints in the road network, a simple topological map is fit for the navigation in the SC areas. While in the LC areas, the navigation of the self-driving vehicle mainly relies on the positioning information. Simultaneous localization and mapping technology is used to provide a detailed metric map in the LC areas, and a window constraint Markov localization algorithm is introduced to achieve accurate position using laser scanner. Furthermore, the real-time performance of the Markov algorithm is enhanced by using a constraint window to restrict the size of the state space. By registering the metric maps into the road network, a hybrid map of the urban scenario can be constructed. Real unmanned vehicle mapping and navigation tests demonstrated the capabilities of the proposed method.

Autonomous-driving technologies have attracted considerable academic and industrial interests in recent years. Navigating an unmanned ground vehicle (UGV) driving through urban areas is a difficult task. The UGV must know exactly where it is in dynamic traffic scenarios. The accuracy of the location information directly impacts the safety and control stability of the UGV.

The localization methods of autonomous driving can be divided into satellite signal-based solutions and map matching solutions. In satellite signal-based solutions, GPS is the most common positioning way. However, because of the multipath effect and the occlusion of satellite signals caused by buildings, trees or clouds, the ordinary GPS equipment with normal accuracy cannot meet the requirement of the positioning system. Therefore, the differential GPS (DGPS) or GPS-inertial navigation system (INS) positioning system comes into use. In the 2007 Defense Advanced Research Projects Agency (DARPA) Urban Challenge, most of the unmanned vehicles were equipped with costly DGPS-based navigation systems [

For the map matching-based method, an unmanned vehicle gets its position by comparing the environment map with real-time perception data from mounted sensors, such as laser scanners or cameras [

From the experiences of participation in the Intelligent Vehicle Future Challenge competitions of China since 2009 [

Based on the above analysis, our aim is to build a low-cost hybrid map-based navigation system for a UGV self-driving in urban scenarios, as shown in

In order to reduce the dependence on a high-precision positioning system and the amount of map data, we divide the whole urban space into two different areas. Because the traffic lanes can provide forward direction for a UGV, the district with visible lanes is defined as a strong constraint (SC) area. On the other hand, the district without lanes is defined as a loose constraint (LC) area. As shown in

The global path for a UGV self-driving navigation in either SC areas or LC areas is described in the topological map. Basic elements of the topological map include nodes and edges. Nodes represent significant places in the map, and edges show the relationship between two nodes. In this work, the nodes are defined as GPS positions of LC areas, and the edges represent certain roads between two adjacent nodes.

The common way to get the GPS positions is to drive the vehicle with a GPS receiver and record key waypoints on the routes. In a small area, the GPS data collection work can be done manually. However, when building a whole city map of urban size, the work may be too tedious and time-consuming. Therefore, we extract the urban topological structure from OpenStreetMap (OSM) [

In the XML file structure shown in

For LC areas, there are no lanes to guide the UGV. When the vehicle enters into these areas, either the sparse GPS waypoints in the topological map or the positioning error could cause unstable driving behavior. Therefore, the primary task is to add more environmental constraints to restrict the vehicle movement. The constraints are added in the form of metric map. Additionally, the local metric map can be used for localization, which will be discussed in Section 3.

For environment mapping, there have been many impressive results shown in recent years [

Given that the observation, _{t}_{t}

Assume the environment is static. When the map, _{t}_{1:}_{t}_{–1}, and measurements, _{1:}_{t}_{–1},

Based on reference [

The inverse sensor model, _{t}_{t}

Calculating a vehicle’s pose is a maximum likelihood estimation problem in the local mapping method, which is finding the most likely pose of the vehicle in the configuration space. Given the posterior likelihood of the vehicle’s pose at the last time step, _{t–1}, the _{t}_{t–1}, _{t}

The pose estimation is resolved using a particle filter. Initially, particles are uniformly distributed in the area without any prior knowledge of the vehicle’s position. A simplified velocity model is employed to predict the particle’s motion:

The local map method simplifies the map generation by decoupling pose estimation from a map update. This may introduce errors in the generated map, but the errors are acceptable.

In the map update step, cells occupied with moving obstacles have to be removed in

To integrate the local metric map with the urban topological map, we collect some GPS positioning data in the area during a local metric map mapping process. After the local map is established, only one GPS location in each local map is selected as a registration point, which can be used to register the local metric map to a nearest node in the topological map. The registration point’s position is also stored in the corresponding node.

In Section 2, we have constructed the topological map of the urban scenario and the local metric maps of the LC areas. In the SC area, our previous works can get enough information, including detections of lane markings [

There are lots of map-matching-based localization methods. In reference [

The corresponding metric map will be loaded into memory when the vehicle is approaching a node in the digital map. Map matching-based localization will be launched when the vehicle approaches the scope of the metric map and registers the vehicle with the local environment, as location A in

Following the global path in the topological map, the autonomous vehicle will drive into the expected edge path of the digital map when it leaves the local metric map. The local metric map contains not only the static structure of an area, which is used for localization and obstacle avoidance, but also the connections with edges in the digital map. This information is recorded when the map is generated. The connection with the edge on the global path can be used as the target state in the vehicle’s configuration space for local path planning. Once strong constrained elements are detected, when the vehicle is driving along the local path, the driving mode will be switched to strong constrained mode, for example, as shown in

The original Markov localization method needs to maintain a likelihood matrix with the same size of the entire metric map. Except for elements closed to the true position, the probabilities of most elements in the matrix are negligible, but they have to be updated every time after a new measurement is made. This updated probability can be expressed as _{t}_{1},…,_{t}_{1},…,_{t− 1}), where _{t}

For the simplification of real-time computation, with the Markov assumption of the vehicle’s movement and the independence of environment perception on the vehicle’s previous position, the expression can be further simplified as:
_{t}_{t− 1},_{t− 1}) and _{t}|L_{t}

The Markov localization method is generally used for indoor robotics. When it is applied in a large space, the main problem is low positioning frequency caused by increased state space for computation. With the assumption that the vehicle’s true position is close to its GPS indications, we propose a window constraint Markov localization (WCML) method, using a small likelihood matrix to reduce the computational cost in the Markov localization process.

As shown in

In the window constraint Markov localization process, the states needed to be updated in the state space are limited inside the window. Therefore, the movement model of Markov localization can be rewritten as:
_{t}_{t− 1}_{t}_{− 1}_{1}_{t− 1}_{1}_{t}_{− 2}) for the expression.

Another part of _{t}|L_{t}_{t}_{t}_{t}

The Beijing Institute of Technology unmanned ground vehicle (BITUGV) as shown in

Other equipment for UGV positioning consists of one GPS receiver, one inertial navigation system (INS) and one odometer. The GPS receiver provides the position to locate the UGV in the road network. The INS mainly provides the heading direction of the UGV. The odometer records the distance the UGV traveled. It should be noted that the GPS receiver’s accuracy we used is relatively low. The positioning error is about 3 m at least.

The quantitative analysis of the WCML process is summarized in

The experiment results and analysis show that the WCML method has the ability for UGV to localize in the loose constraint area. When entering the LC area, the positioning system adapts itself and corrects the initial positioning error before getting into the intersection area. It also can provide about a 10 Hz localization frequency for UGV, which is enough for relatively low speed driving in intersections. Results in

This paper proposes a hybrid map-based navigation method for unmanned ground vehicles in the urban scenario, without using accurate positioning equipment. We divide the urban scenario into strong constraint areas, where the traffic lanes exist, and loose constraint areas, where no lanes can guide the vehicle. In the loose constraint area, the Markov localization method is involved to achieve high positioning accuracy. By combing the local metric maps for loose constraint areas and the global topological map, a hybrid map is established. In our experiment, we have successfully reduced 50% of the map data. If the scale of the scenario is enlarged, the reduction effect could be more obvious.

In order to improve the real-time performance of grid-based Markov localization, we introduce a window constraint Markov localization method for accurate positioning. A constraint window moving with the vehicle can restrict the size of the state space and, thus, reduce the computational cost. According to our experiments, most positioning results can be achieved within 0.1 s. The update frequency and positioning accuracy can meet the navigation requirements for relatively low speed driving in loose constraint areas.

For future research, we will try to involve vision features to improve the performance of the Markov localization process in the environment map building. Furthermore, the vehicle-to-infrastructure and vehicle-to-vehicle [

This study is supported by the National Natural Science Foundation of China (No. 91120010 and No. 51275041), the Doctoral Fund of Ministry of Education of China (No. 20121101120015) and the Fundamental Research Funds from Beijing Institute of Technology (No. 20120342011).

The authors declare no conflict of interest.

Topological-metric hybrid map for navigation.

Definitions of strong constraint area and loose constraint area.

Treelike structure of OpenStreetMap data.

Illustration of building topological map by OpenStreetMap (OSM). (

Navigation methods switching.

Constraint window in the grid map of the loose constraint (LC) area.

Selection of spare directions for ray tracing. The directions in the scanning scale of the laser sensor are chosen for the ray tracing operation.

The Beijing Institute of Technology Unmanned Ground Vehicle. (

Metric-topological-based hybrid map of experiment environment. (

(

(

Probability distribution in constraint window scale during the WCML process. Brighter color shows higher probability. The distribution is changed from a decentralized state to a centralized state. The centralized state in t = 9 s shows that the vehicle finally finds its position.

Window constraint Markov localization in loose constraint areas.