Multi-object tracking is a crucial problem for autonomous vehicle. Most state-of-the-art approaches adopt the tracking-by-detection strategy, which is a two-step procedure consisting of the detection module and the tracking module. In this paper, we improve both steps. We improve the detection module by incorporating the temporal information, which is beneficial for detecting small objects. For the tracking module, we propose a novel compressed deep Convolutional Neural Network (CNN) feature based Correlation Filter tracker. By carefully integrating these two modules, the proposed multi-object tracking approach has the ability of re-identification (ReID) once the tracked object gets lost. Extensive experiments were performed on the KITTI and MOT2015 tracking benchmarks. Results indicate that our approach outperforms most state-of-the-art tracking approaches.

For autonomous driving, it is important to navigate in an unknown environment. In this paper, we propose a fully automated 2D simultaneous localization and mapping (SLAM) system based on lidar working in large-scale outdoor environments. To improve the accuracy and robustness of the scan matching module, an improved Correlative Scan Matching (CSM) algorithm is proposed. For efficient place recognition, we design an AdaBoost based loop closure detection algorithm which can efficiently reject false loop closures. For the SLAM back-end, we propose a light-weight graph optimization algorithm based on incremental smoothing and mapping (iSAM). The performance of our system is verified on various large-scale datasets including our real-world datasets and the KITTI odometry benchmark. Further comparisons to the state-of-the-art approaches indicate that our system is competitive with established techniques.

Range images are commonly used representations for 3D LiDAR point cloud in the field of autonomous driving. The approach of generating a range image is generally regarded as a standard approach. However, there do exist two different types of approaches to generating the range image: In one approach, the row of the range image is defined as the laser ID, and in the other approach, the row is defined as the elevation angle. We named the first approach Projection By Laser ID (PBID), and the second approach Projection By Elevation Angle (PBEA). Few previous works have paid attention to the difference of these two approaches. In this work, we quantitatively analyze these two different approaches. Experimental results show that the PBEA approach can obtain much smaller quantization errors than PBID, and should be the preferred choice in reconstruction-related tasks. If PBID is chosen for use in recognition-related tasks, then we have to tolerate its larger quantization error.

For autonomous driving, it is important to obtain precise and high-frequency localization information. This paper proposes a novel method in which the Inertial Measurement Unit (IMU), wheel encoder, and lidar odometry are utilized together to estimate the ego-motion of an unmanned ground vehicle. The IMU is fused with the wheel encoder to obtain the motion prior, and it is involved in three levels of the lidar odometry: Firstly, we use the IMU information to rectify the intra-frame distortion of the lidar scan, which is caused by the vehicle’s own movement; secondly, the IMU provides a better initial guess for the lidar odometry; and thirdly, the IMU is fused with the lidar odometry in an Extended Kalman filter framework. In addition, an efficient method for hand–eye calibration between the IMU and the lidar is proposed. To evaluate the performance of our method, extensive experiments are performed and our system can output stable, accurate, and high-frequency localization results in diverse environment without any prior information.

Three-dimensional (3D) point cloud maps are widely used in autonomous driving scenarios. These maps are usually generated by accumulating sequential LiDAR scans. When generating a map, moving objects (such as vehicles or moving pedestrians) will leave long trails on the assembled map. This is undesirable and reduces the map quality. In this paper, we propose MapCleaner, an approach that can effectively remove the moving objects from the map. MapCleaner first estimates a dense and continuous terrain surface, based on which the map point cloud is then divided into a noisy part below the terrain, the terrain, and the object part above the terrain. Next, a specifically designed moving points identification algorithm is performed on the object part to find moving objects. Experiments are performed on the SemanticKITTI dataset. Results show that the proposed MapCleaner outperforms state-of-the-art approaches on all five tested SemanticKITTI sequences. MapCleaner is a learning-free method and has few parameters to tune. It is also successfully evaluated on our own dataset collected with a different type of LiDAR.