相关论文: A Survey on 3D LiDAR Localization for Autonomous V…
Online localization of road intersections is beneficial for autonomous vehicle localization, mapping and motion planning. Intersections offer strong landmarks for correcting vehicle pose estimation, anchoring new sensor data in up-to-date…
Detecting objects such as cars and pedestrians in 3D plays an indispensable role in autonomous driving. Existing approaches largely rely on expensive LiDAR sensors for accurate depth information. While recently pseudo-LiDAR has been…
Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning. Conventional localization devices such as Global…
Nowadays, there are outstanding strides towards a future with autonomous vehicles on our roads. While the perception of autonomous vehicles performs well under closed-set conditions, they still struggle to handle the unexpected. This survey…
Autonomous vehicles rely on perception systems to understand their surroundings for further navigation missions. Cameras are essential for perception systems due to the advantages of object detection and recognition provided by modern…
The vehicle's perception sensors radar, lidar and camera, which must work continuously and without restriction, especially with regard to automated/autonomous driving, can lose performance due to unfavourable weather conditions. This paper…
3D detection of traffic management objects, such as traffic lights and road signs, is vital for self-driving cars, particularly for address-to-address navigation where vehicles encounter numerous intersections with these static objects.…
The ability to detect and segment moving objects in a scene is essential for building consistent maps, making future state predictions, avoiding collisions, and planning. In this paper, we address the problem of moving object segmentation…
Due to their ubiquity and long-term stability, pole-like objects are well suited to serve as landmarks for vehicle localization in urban environments. In this work, we present a complete mapping and long-term localization system based on…
Multiple LiDARs have progressively emerged on autonomous vehicles for rendering a wide field of view and dense measurements. However, the lack of precise calibration negatively affects their potential applications in localization and…
State estimation of oncoming vehicles: Earlier research has been based on determining states like position, velocity, orientation , angular velocity, etc of ego-vehicle. Our approach focuses on estimating the states of non-ego vehicles…
3D object detection from images, one of the fundamental and challenging problems in autonomous driving, has received increasing attention from both industry and academia in recent years. Benefiting from the rapid development of deep…
LiDAR has become a standard sensor for autonomous driving applications as they provide highly precise 3D point clouds. LiDAR is also robust for low-light scenarios at night-time or due to shadows where the performance of cameras is…
Autonomous vehicles require accurate and robust localization and mapping algorithms to navigate safely and reliably in urban environments. We present a novel sensor fusion-based pipeline for offline mapping and online localization based on…
This paper presents a Light Detection and Ranging (LiDAR) data set that targets complex urban environments. Urban environments with high-rise buildings and congested traffic pose a significant challenge for many robotics applications. The…
This paper presents Lidar-based Simultaneous Localization and Mapping (SLAM) for autonomous driving vehicles. Fusing data from landmark sensors and a strap-down Inertial Measurement Unit (IMU) in an adaptive Kalman filter (KF) plus the…
Autonomous operation of UAVs in a closed environment requires precise and reliable pose estimate that can stabilize the UAV without using external localization systems such as GNSS. In this work, we are concerned with estimating the pose…
In the typical urban intersection scenario, both vehicles and infrastructures are equipped with visual and LiDAR sensors. By successfully integrating the data from vehicle-side and road monitoring devices, a more comprehensive and accurate…
The localization of self-driving cars is needed for several tasks such as keeping maps updated, tracking objects, and planning. Localization algorithms often take advantage of maps for estimating the car pose. Since maintaining and using…
LiDAR is used in autonomous driving to provide 3D spatial information and enable accurate perception in off-road environments, aiding in obstacle detection, mapping, and path planning. Learning-based LiDAR semantic segmentation utilizes…