Robust and Efficient Vehicles Motion Estimation with LowCost MultiCamera and OdometerGyroscope In this paper, we present a robust and efficient estimation approach with multicamera, odometer and gyroscope. Robust initialization, tightlycoupled optimization estimator and multicamera loopclosure detection are utilized in the proposed approach. In initialization, the measurements of odometer and gyroscope are used to compute scale, and then estimate the bias of sensors. In estimator, the preintegration of odometer and gyroscope is derived and combined with the measurements of multicamera to estimate the motion in a tightlycoupled optimization framework. In loopclosure detection, a connection between different cameras of the vehicle can be built, which significantly improve the success rate of loopclosure detection. The proposed algorithm is validated in multiple realworld datasets collected in different places, time, weather and illumination. Experimental results show that the proposed approach can estimate the motion of vehicles robustly and efficiently.

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.