Fast Detection of Moving Traffic Participants in LiDAR Point Clouds by using Particles augmented with Free Space Information

Reich, Andreas; Wuensche, Hans-Joachim

doi:10.1109/iros47612.2022.9982239

Cited by 4 publications

(1 citation statement)

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“…Using predicted bounding boxes to filter out non-static keypoints, YOLOPoint shows the best accuracy to speed trade-off of all tested methods. Future work will concentrate on incorporating YOLOPoint into our SLAM framework [3] by using keypoints and static objects as landmarks and increasing the robustness of object tracking [18] by matching keypoints.…”

Section: Discussionmentioning

confidence: 99%

YOLOPoint: Joint Keypoint and Object Detection

Backhaus,

Luettel,

Wuensche

2023

Lecture Notes in Computer Science

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Intelligent vehicles of the future must be capable of understanding and navigating safely through their surroundings. Camerabased vehicle systems can use keypoints as well as objects as low-and high-level landmarks for GNSS-independent SLAM and visual odometry. To this end we propose YOLOPoint, a convolutional neural network model that simultaneously detects keypoints and objects in an image by combining YOLOv5 and SuperPoint to create a single forward-pass network that is both real-time capable and accurate. By using a shared backbone and a light-weight network structure, YOLOPoint is able to perform competitively on both the HPatches and KITTI benchmarks.

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Section: Discussionmentioning

confidence: 99%