Sensor Fusion is the broad category of combining various on-board sensors to produce better measurement estimates. These sensors are combined to compliment each other and overcome individual shortcomings.
We highlight that our system is a general framework, which can easily fuse various global sensors in a unified pose graph optimization.
In this paper, we provide a sensor fusion scheme integrating camera videos, consumer-grade motion sensors (GPS/IMU), and a 3D semantic map in order to achieve robust self-localization and semantic segmentation for autonomous driving.
2) One-click annotation: Instead of drawing 3D bounding boxes or point-wise labels, we simplify the annotation to just one click on the target object, and automatically generate the bounding box for the target.
Image-based fiducial markers are widely used in robotics and computer vision problems such as object tracking in cluttered or textureless environments, camera (and multi-sensor) calibration tasks, or vision-based simultaneous localization and mapping (SLAM).
In our experiment, compared with the traditional method of offloading raw sensor data to be processed in the cloud, DDNN locally processes most sensor data on end devices while achieving high accuracy and is able to reduce the communication cost by a factor of over 20x.
We dub this problem amodal scene layout estimation, which involves "hallucinating" scene layout for even parts of the world that are occluded in the image.
Region proposal algorithms play an important role in most state-of-the-art two-stage object detection networks by hypothesizing object locations in the image.
Ranked #1 on 3D Object Detection on nuScenes-F