Related papers: Efficient Extrinsic Calibration of Multi-Sensor 3D…
LiDAR-camera extrinsic calibration (LCEC) is crucial for multi-modal data fusion in autonomous robotic systems. Existing methods, whether target-based or target-free, typically rely on customized calibration targets or fixed scene types,…
Multiple lidars are prevalently used on mobile vehicles for rendering a broad view to enhance the performance of localization and perception systems. However, precise calibration of multiple lidars is challenging since the feature…
In multimodal perception systems, achieving precise extrinsic calibration between LiDAR and camera is of critical importance. Previous calibration methods often required specific targets or manual adjustments, making them both…
Many robotics and mapping systems contain multiple sensors to perceive the environment. Extrinsic parameter calibration, the identification of the position and rotation transform between the frames of the different sensors, is critical to…
This paper presents a novel automatic calibration system to estimate the extrinsic parameters of LiDAR mounted on a mobile platform for sensor misalignment inspection in the large-scale production of highly automated vehicles. To obtain…
Accurate calibration of sensor extrinsic parameters for ground robotic systems (i.e., relative poses) is crucial for ensuring spatial alignment and achieving high-performance perception. However, existing calibration methods typically…
Achieving safe and reliable autonomous driving relies greatly on the ability to achieve an accurate and robust perception system; however, this cannot be fully realized without precisely calibrated sensors. Environmental and operational…
Reliable operation in inclement weather is essential to the deployment of safe autonomous vehicles (AVs). Robustness and reliability can be achieved by fusing data from the standard AV sensor suite (i.e., lidars, cameras) with weather…
LiDAR-camera extrinsic calibration is essential for multi-modal data fusion in robotic perception systems. However, existing approaches typically rely on handcrafted calibration targets (e.g., checkerboards) or specific, static scene types,…
Accurate extrinsic calibration of multiple LiDARs is crucial for improving the foundational performance of three-dimensional (3D) map reconstruction systems. This paper presents a novel targetless extrinsic calibration framework for…
Combining multiple LiDARs enables a robot to maximize its perceptual awareness of environments and obtain sufficient measurements, which is promising for simultaneous localization and mapping (SLAM). This paper proposes a system to achieve…
There has been significant progress made in the field of autonomous vehicles. Object detection and tracking are the primary tasks for any autonomous vehicle. The task of object detection in autonomous vehicles relies on a variety of sensors…
Environment perception is a key component of any autonomous system and is often based on a heterogeneous set of sensors and fusion thereof for which sensor sensor calibration plays fundamental role. It can be divided to intrinsic and…
LiDAR sensors are a key modality for 3D perception, yet they are typically designed independently of downstream tasks such as point cloud registration. Conventional registration operates on pre-acquired datasets with fixed LiDAR…
Sensor-based environmental perception is a crucial part of the autonomous driving system. In order to get an excellent perception of the surrounding environment, an intelligent system would configure multiple LiDARs (3D Light Detection and…
We demonstrate a multi-lidar calibration framework for large mobile platforms that jointly calibrate the extrinsic parameters of non-overlapping Field-of-View (FoV) lidar sensors, without the need for any external calibration aid. The…
This paper proposes FAST-Calib, a fast and user-friendly LiDAR-camera extrinsic calibration tool based on a custom-made 3D target. FAST-Calib supports both mechanical and solid-state LiDARs by leveraging an efficient and reliable edge…
Multi-modal object detection in autonomous driving has achieved great breakthroughs due to the usage of fusing complementary information from different sensors. The calibration in fusion between sensors such as LiDAR and camera was always…
Localization, or position fixing, is an important problem in robotics research. In this paper, we propose a novel approach for long-term localization in a changing environment using 3D LiDAR. We first create the map of a real environment…
Conventional single LiDAR systems are inherently constrained by their limited field of view (FoV), leading to blind spots and incomplete environmental awareness, particularly on robotic platforms with strict payload limitations. Integrating…