Abstract: The feature pyramid, which is a vital component of the convolutional neural networks, plays a significant role in several perception tasks, including object detection for autonomous driving. However, how to better fuse multi-level and multi-sensor feature pyramids is still a significant challenge, especially for object detection.
Abstract: Current learning-based 3-D object detection accuracy is heavily impacted by the annotation quality. It is still a challenge to expect an overall high detection accuracy for all classes under different scenarios given the dataset sparsity.
Abstract: Recently significant progress has been made in 3D detection. However, it is still challenging to detect small contour objects under complex scenes. This paper proposes a novel Attention-based Multi-phase Multi-task Fusion (AMMF) that uses point-level, RoI-level, and multi-task fusions to complement the disadvantages of LiDAR and camera, to solve this challenge.
Abstract: In recent years, semi-supervised learning has been widely explored and shows excellent data efficiency for 2D data. There is an emerging need to improve data efficiency for 3D tasks due to the scarcity of labeled 3D data.
Abstract: Accurate localization on the autonomous driving cars is essential for autonomy and driving safety, especially for complex urban streets and search-and-rescue subterranean environments where high-accurate GPS is not available. However current odometry estimation may introduce the drifting problems in long-term navigation without robust global localization.
Abstract: This paper proposes a real-time multi-scale semantic segmentation network (MsNet). MsNet is a combination of our novel multi-scale fusion with matching attention model (MFMA) as the decoding network and the network searched by asymptotic neural architecture search (ANAS) or MobileNetV3 as the encoding network.
Abstract: Autonomous wheelchairs can address a very large need in many populations by serving as the gateway to a much higher degree of independence and mobility capability. This is due to the fact that the big picture idea for autonomous wheelchairs integration into the transportation chain is to allow for individuals to be able to utilize the Intelligent wheelchair to reach the vehicle (regardless of terrain), mount into autonomous wheelchair that navigates to desired destination, and finally autonomous wheelchair dismounts.
Abstract: The fundamental aspect of unmanned ground vehicle (UGV) navigation, especially over off-road environments, are representations of terrain describing geometry, types, and traversability. One of the typical representations of the environment is digital surface models (DSMs) which efficiently encode geometric information.
Abstract: The concrete aging problem has gained more attention in recent years as more bridges and tunnels in the United States lack proper maintenance. Though the Federal Highway Administration requires these public concrete structures to be inspected regularly, on-site manual inspection by human operators is time-consuming and labor-intensive.
Abstract: Handicapped individuals often rely heavily on various assistive technologies including wheelchairs and the purpose of these technologies is to enable greater levels of independence for the user. In the development of autonomous wheelchairs, it is imperative that the wheelchair maintains appropriate stability for the user in an outdoor urban environment.