A MultiSensor Fusion Based 2DDriven 3D Object Detection Approach for Large Scene Applications xe2x88x97 In this paper, we propose a 3D object detection approach for largescale environments by considering the 2D visual information and 3D lidar data simultaneously. In the proposed multisensor fusion framework, first of all, a largescene application oriented data alignment method is presented, which utilizes the EPnP based joint calibration method in order to achieve an improved dataassociation performance. Based on which, the 2D bounding box of each object is generated by using the visual detection and the 3D point cloud contained in the corresponding 3D frustum can be obtained. Then, after the ground fitting and point cloud clustering operations, an innovative scoring mechanism is proposed to evaluate each point cloud cluster and find out the cluster belonging to the detected object. Finally, the accurate lidar point cloud segment in the 3D frustum can be generated. Comparing with existing 3D object detection approaches, we propose a more general framework in this paper, which does not require a large number of 3D point cloud detection datasets to train the network and can be used on the 3D lidar with any number of lines, thus a realtime 3D detection system can be achieved. Experiments in largescale airport environments validate the advanced performance of the proposed approach.

Multipleobject Tracking based on Monocular Camera and 3D Lidar Fusion for Autonomous Vehicles This article describes a multiobject tracking method through sensor fusion with a monocular camera and a 3D Lidar for autonomous vehicles. Specifically, several pairwise costs from information, such as locations, movements, and poses of 3D cues, are designed for tracking. These costs can complement each other to reduce matching errors during the tracking process. Moreover, they are efficient to be online computed with embedded equipment. We feed the pairwise costs to the dataassociation framework, which is based on the Hungarian algorithm, and then do the backend fusion for the tracking results. The experimental results on our autonomous sightseeing car demonstrate that our tracking method could achieve accurate and robust results in realworld traffic scenarios.