This paper addresses the problem of image based localization. The goal is to find quickly and accurately the relative pose from a query taken from a stereo camera and a map obtained using visual SLAM which contains poses and 3D points associated to descriptors. In this paper we introduce a new method that leverages the stereo vision by adding geometric information to visual descriptors. This method can be used when the vertical direction of the camera is known (for example on a wheeled robot). This new geometric visual descriptor can be used with several image based localization algorithms based on visual words. We test the approach with different datasets (indoor, outdoor) and we show experimentally that the new geometric-visual descriptor improves standard image based localization approaches.

In this paper, we propose to calibrate a vehicle equipped with several 2D-LIDARs and cameras. Our goal is to offer a method which is easy to use with very little manual intervention and which doesn't need a special calibration target. Sensor data is recorded while the vehicle is driven along a trajectory in a man made environment with vertical walls. The first step is a classical autocalibration approach based on visual SLAM and bundle adjustment is used to recover the cameras internal and external parameters. The second step which is the main contribution of the paper, is a novel optimisation algorithm to compute the external parameters of the LIDAR. We present the methodology in its entirety because the user has to do a single data acquisition for camera and LIDAR calibration. Our approach is validated by real experimental results.

This paper addresses platooning navigation as part of new transportation services emerging nowadays in urban areas. Platooning formation is ensured using a global decentralized control strategy supported by inter-vehicle communications. A large motion flexibility is achieved according to a manual guidance mode, i.e. the path to follow is inferred online from the motion of the manually driven first vehicle. For this purpose, a visual SLAM algorithm that relies on monocular vision is run on the lead vehicle and coupled with a trajectory creation procedure. Both the map and trajectory updates are shared online with the following vehicles and permit them to derive their absolute location with respect to a common reference trajectory from their current camera image. Full-scale experiments with two urban vehicles demonstrate the performance of the proposed approach.

This article deals with a simple and flexible extrinsic calibration method, for non-overlapping camera rig. The cameras do not see the same area at the same time. They are rigidly linked and can be moved. The most representative application is the mobile robotics domain. The calibration procedure consists in maneuvering the system while each camera observes a static scene. A linear solution derived from hand eye calibration scheme is proposed to compute an initial estimate of the extrinsic parameters. The main contribution is a specific bundle adjustment which refines both the scene geometry and the extrinsic parameters. Finally, an efficient implementation of the specific bundle adjustment step is defined for online calibration purpose. The proposed approach is validated with both synthetic and real data.

Multi-camera systems are more and more used in vision-based robotics. An accurate extrinsic calibration is usually required. In most of cases, this task is done by matching features through different views of the same scene. However, if the cameras fields of view do not overlap, such a matching procedure is not feasible anymore. This article deals with a simple and flexible extrinsic calibration method, for nonoverlapping camera rig. The aim is the calibration of non-overlapping cameras embedded on a vehicle, for visual navigation purpose in urban environment. The cameras do not see the same area at the same time. The calibration procedure consists in manoeuvring the vehicle while each camera observes a static scene. The main contributions are a study of the singular motions and a specific bundle adjustment which both reconstructs the scene and calibrates the cameras. Solutions to handle the singular configurations, such as planar motions, are exposed. The proposed approach has been validated with synthetic and real data.

In this system paper, we propose a real-time car localisation process in dense urban areas by using a single perspective camera and a priori on the environment. To tackle this problem, it is necessary to solve two well-known monocular SLAM limitations: scale factor drift and error accumulation. The proposed idea is to combine a monocular SLAM process based on bundle adjustment with simple knowledge, i.e. the position and orientation of the camera with regard to the road and a coarse 3D model of the environment, as those provided by GIS database. First, we show that, thanks to specific SLAM-based constraints, the road homography can be expressed only with respect to the scale factor parameter. This allows the scale factor to be robustly and frequently estimated. Then, we propose to use the global information brought by 3D city models in order to correct the monocular SLAM error accumulation. Even with coarse 3D models, turnings give enough geometrical constraints to allow fitting the reconstructed 3D point cloud with the 3D model. Experiments on large-scale sequences (several kilometres) show that the entire process permits the real-time localisation of a car in city centre, even in real traffic condition.

Multi-camera systems are used in many domains such as vision-based robotics or video surveillance. An accurate extrinsic calibration is usually required. In most of cases, this task is done by matching features through different views of the same scene. However, if the cameras' fields of view do not overlap, such a matching procedure is not feasible anymore. Despite this constraint, this article deals with a simple and flexible extrinsic calibration method. The main contribution is the use of an unknown geometry scene and a planar mirror to create an overlap between views of the different cameras. Furthermore, the impact of the mirror refraction is also studied. The aim is the calibration of two non-overlapping cameras embedded on a vehicle, for visual navigation purpose in urban environment. The proposed approaches have been validated with both synthetic and real data in a metrological experimental framework.

This paper addresses the challenging issue of vision-based localization in urban context. It briefly describes our contributions in large environments modeling and accurate camera localization. The efficiency of the resulting system is illustrated through Augmented Reality results on large trajectory of several hundred meters.

In the past few years, lots of works were achieved on Simultaneous Localization and Mapping (SLAM). It is now possible to follow in real time the trajectory of a moving camera in an unknown environment. However, current SLAM methods are still prone to drift errors, which prevent their use in large-scale applications. In this paper, we propose a solution to reduce those errors a posteriori. Our solution is based on a postprocessing algorithm that exploits additional geometric constraints, relative to the environment, to correct both the reconstructed geometry and the camera trajectory. These geometric constraints are obtained through a coarse 3D modelisation of the environment, similar to those provided by GIS database. First, we propose an original articulated transformation model in order to roughly align the SLAM reconstruction with this 3D model through a non-rigid ICP step. Then, to refine the reconstruction, we introduce a new bundle adjustment cost function that includes, in a single term, the usual 3D point/ID observation consistency constraint as well as the geometric constraints provided by the 3D model. Results on large-scale synthetic and real sequences show that our method successfully improves SLAM reconstructions. Besides, experiments prove that the resulting reconstruction is accurate enough to be directly used for global relocalization applications.

To address traffic saturation in cities, new ldquourban transportation systemsrdquo, based on electric vehicles in free-access, are in developing. One necessary functionality of such systems is their ability to move in a platoon fashion. A global decentralized platoon control strategy, supported by inter-vehicle communications, is addressed in this paper, relying on nonlinear control techniques. The main interest in a global approach is that servoing error accumulation can be avoided, whatever the platoon length. However, absolute vehicle localization is then required. In urban applications, cameras are realistic sensors, but localization is supplied in a 3D visual virtual world, slightly distorted w.r.t. the actual metric one. To enable accurate guidance, local corrections to the visual world are here computed from the data supplied by a laser rangefinder mounted on the second vehicle, and then shared with the whole platoon. Full-scale experiments demonstrate the performance of the proposed approach.

In this paper we present a method for computing the localization of a mobile robot with reference to a learning video sequence. The robot is first guided on a path by a human, while the camera records a monocular learning sequence. Then a 3D reconstruction of the path and the environment is computed off line from the learning sequence. The 3D reconstruction is then used for computing the pose of the robot in real time (30 Hz) in autonomous navigation. Results from our localization method are compared to the ground truth measured with a differential GPS.

In this paper, a complete system for outdoor robot navigation is presented. It uses only monocular vision. The robot is first guided on a path by a human. During this learning step, the robot records a video sequence. From this sequence, a three dimensional map of the trajectory and the environment is built. When this map has been computed, the robot is able to follow the same trajectory by itself. Experimental results carried out with an urban electric vehicle are shown and compared to the ground truth.

In this paper we present a method for computing the localization of a mobile robot with reference to a learning video sequence. The robot is first guided on a path by a human, while the camera records a monocular learning sequence. Then the computer builds a map of the environment. This is done by first extracting key frames from the learning sequence. Then the epipolar geometry and camera motion are computed between key frames. Additionally a hierarchical bundle adjustment is used to refine the reconstruction. The map stored for the localization include the position of the camera associated with each key frame as well as a set of interest points detected in the images and reconstructed in 3D. Using this map it is possible to compute the localization of the robot in real time during the automatic driving phase. 1