Robust and accurate depth estimation by fusing LiDAR and Stereo
Depth estimation is one of the key technologies in some fields such as autonomous driving and robot navigation. However, the traditional method of using a single sensor is inevitably limited by the performance of the sensor. Therefore, a precision and robust method for fusing the LiDAR and stereo cameras is proposed. This method fully combines the advantages of the LiDAR and stereo camera, which can retain the advantages of the high precision of the LiDAR and the high resolution of images respectively. Compared with the traditional stereo matching method, the texture of the object and lighting conditions have less influence on the algorithm. Firstly, the depth of the LiDAR data is converted to the disparity of the stereo camera. Because the density of the LiDAR data is relatively sparse on the y-axis, the converted disparity map is up-sampled using the interpolation method. Secondly, in order to make full use of the precise disparity map, the disparity map and stereo matching are fused to propagate the accurate disparity. Finally, the disparity map is converted to the depth map. Moreover, the converted disparity map can also increase the speed of the algorithm. We evaluate the proposed pipeline on the KITTI benchmark. The experiment demonstrates that our algorithm has higher accuracy than several classic methods.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous DrivingDepth EstimationRobot NavigationStereo MatchingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Real Time Dense Depth Estimation by Fusing Stereo with Sparse Depth Measurements
We present an approach to depth estimation that fuses information from a stereo pair with sparse range measurements derived from a LIDAR sensor or a range camera. The goal of this work is to exploit the complementary str…
Depth EstimationPseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving
Detecting objects such as cars and pedestrians in 3D plays an indispensable role in autonomous driving. Existing approaches largely rely on expensive LiDAR sensors for accurate depth information. While recently pseudo-Li…
3D Object Detection3D Object Detection From Stereo ImagesAutonomous DrivingDepth Estimation+3Stereo-LiDAR Depth Estimation with Deformable Propagation and Learned Disparity-Depth Conversion
Accurate and dense depth estimation with stereo cameras and LiDAR is an important task for automatic driving and robotic perception. While sparse hints from LiDAR points have improved cost aggregation in stereo matching,…
Depth EstimationStereo MatchingNon-learning Stereo-aided Depth Completion under Mis-projection via Selective Stereo Matching
We propose a non-learning depth completion method for a sparse depth map captured using a light detection and ranging (LiDAR) sensor guided by a pair of stereo images. Generally, conventional stereo-aided depth completio…
Camera CalibrationDepth CompletionDepth EstimationStereo MatchingDepth Estimation maps of lidar and stereo images
This paper as technology report is focusing on evaluation and performance about depth estimations based on lidar data and stereo images(front left and front right). The lidar 3d cloud data and stereo images are provided …
Depth EstimationStereo Depth Estimation