Timealign: A multi-modal object detection method for time misalignment fusing in autonomous driving
- URL: http://arxiv.org/abs/2412.10033v1
- Date: Fri, 13 Dec 2024 10:48:38 GMT
- Title: Timealign: A multi-modal object detection method for time misalignment fusing in autonomous driving
- Authors: Zhihang Song, Lihui Peng, Jianming Hu, Danya Yao, Yi Zhang,
- Abstract summary: Timealign module predicts and combines LiDAR features with observation to tackle such time misalignment based on SOTA GraphBEV framework.
Our study used historical frames of LiDAR to better align features when the LiDAR data lags exist.
- Score: 7.601405124830806
- License:
- Abstract: The multi-modal perception methods are thriving in the autonomous driving field due to their better usage of complementary data from different sensors. Such methods depend on calibration and synchronization between sensors to get accurate environmental information. There have already been studies about space-alignment robustness in autonomous driving object detection process, however, the research for time-alignment is relatively few. As in reality experiments, LiDAR point clouds are more challenging for real-time data transfer, our study used historical frames of LiDAR to better align features when the LiDAR data lags exist. We designed a Timealign module to predict and combine LiDAR features with observation to tackle such time misalignment based on SOTA GraphBEV framework.
Related papers
- StreamLTS: Query-based Temporal-Spatial LiDAR Fusion for Cooperative Object Detection [0.552480439325792]
We propose Time-Aligned COoperative Object Detection (TA-COOD), for which we adapt widely used dataset OPV2V and DairV2X.
Experiment results confirm the superior efficiency of our fully sparse framework compared to the state-of-the-art dense models.
arXiv Detail & Related papers (2024-07-04T10:56:10Z) - Improved LiDAR Odometry and Mapping using Deep Semantic Segmentation and
Novel Outliers Detection [1.0334138809056097]
We propose a novel framework for real-time LiDAR odometry and mapping based on LOAM architecture for fast moving platforms.
Our framework utilizes semantic information produced by a deep learning model to improve point-to-line and point-to-plane matching.
We study the effect of improving the matching process on the robustness of LiDAR odometry against high speed motion.
arXiv Detail & Related papers (2024-03-05T16:53:24Z) - TimePillars: Temporally-Recurrent 3D LiDAR Object Detection [8.955064958311517]
TimePillars is a temporally-recurrent object detection pipeline.
It exploits the pillar representation of LiDAR data across time.
We show how basic building blocks are enough to achieve robust and efficient results.
arXiv Detail & Related papers (2023-12-22T10:25:27Z) - Traj-LO: In Defense of LiDAR-Only Odometry Using an Effective
Continuous-Time Trajectory [20.452961476175812]
This letter explores the capability of LiDAR-only odometry through a continuous-time perspective.
Our proposed Traj-LO approach tries to recover the spatial-temporal consistent movement of LiDAR.
Our implementation is open-sourced on GitHub.
arXiv Detail & Related papers (2023-09-25T03:05:06Z) - Unsupervised Domain Adaptation for Self-Driving from Past Traversal
Features [69.47588461101925]
We propose a method to adapt 3D object detectors to new driving environments.
Our approach enhances LiDAR-based detection models using spatial quantized historical features.
Experiments on real-world datasets demonstrate significant improvements.
arXiv Detail & Related papers (2023-09-21T15:00:31Z) - TrajMatch: Towards Automatic Spatio-temporal Calibration for Roadside
LiDARs through Trajectory Matching [12.980324010888664]
We propose TrajMatch -- the first system that can automatically calibrate for roadside LiDARs in both time and space.
Experiment results show that TrajMatch can achieve a spatial calibration error of less than 10cm and a temporal calibration error of less than 1.5ms.
arXiv Detail & Related papers (2023-02-04T12:27:01Z) - Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object
Detection [58.81316192862618]
Two critical sensors for 3D perception in autonomous driving are the camera and the LiDAR.
fusing these two modalities can significantly boost the performance of 3D perception models.
We benchmark the state-of-the-art fusion methods for the first time.
arXiv Detail & Related papers (2022-05-30T09:35:37Z) - Stress-Testing LiDAR Registration [52.24383388306149]
We propose a method for selecting balanced registration sets, which are challenging sets of frame-pairs from LiDAR datasets.
Perhaps unexpectedly, we find that the fastest and simultaneously most accurate approach is a version of advanced RANSAC.
arXiv Detail & Related papers (2022-04-16T05:10:55Z) - Learning Moving-Object Tracking with FMCW LiDAR [53.05551269151209]
We propose a learning-based moving-object tracking method utilizing our newly developed LiDAR sensor, Frequency Modulated Continuous Wave (FMCW) LiDAR.
Given the labels, we propose a contrastive learning framework, which pulls together the features from the same instance in embedding space and pushes apart the features from different instances to improve the tracking quality.
arXiv Detail & Related papers (2022-03-02T09:11:36Z) - Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor
Setups [68.8204255655161]
We present a method to calibrate the parameters of any pair of sensors involving LiDARs, monocular or stereo cameras.
The proposed approach can handle devices with very different resolutions and poses, as usually found in vehicle setups.
arXiv Detail & Related papers (2021-01-12T12:02:26Z) - LIBRE: The Multiple 3D LiDAR Dataset [54.25307983677663]
We present LIBRE: LiDAR Benchmarking and Reference, a first-of-its-kind dataset featuring 10 different LiDAR sensors.
LIBRE will contribute to the research community to provide a means for a fair comparison of currently available LiDARs.
It will also facilitate the improvement of existing self-driving vehicles and robotics-related software.
arXiv Detail & Related papers (2020-03-13T06:17:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.