Image sensor data fusion using factorized Kalman filter

R, Roopa H; Parimala, P; Raol, Jitendra R.

doi:10.1109/rteict.2016.7808025

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2019

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“…H. Song [14] proposes the fuse of RGBD and 2D-LiDAR for tracking purposes. Roopa et al [15] fuse images using Kalman Filter (KF) to get more information about the localization of a target, this approach is applied to different cameras and different localization. In [16] the authors fuse with a KF three distance sensors in order to obtain the distance and orientation with respect to a wall.…”

Section: A Previous Workmentioning

confidence: 99%

Depth map estimation methodology for detecting free-obstacle navigation areas

Trejo

Martinez

Flores

2019

2019 International Conference on Unmanned Aircraft Systems (ICUAS)

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This paper presents a vision-based methodology which makes use of a stereo camera rig and a one dimension LiDAR to estimate free obstacle areas for quadrotor navigation. The presented approach fuses information provided by a depth map from a stereo camera rig, and the sensing distance of the 1D-LiDAR. Once the depth map is filtered with a Weighted Least Squares filter (WLS), the information is fused through a Kalman filter algorithm. To determine if there is a free space large enough for the quadrotor to pass through, our approach marks an area inside the disparity map by using the Kalman Filter output information. The whole process is implemented in an embedded computer Jetson TX2 and coded in the Robotic Operating System (ROS). Experiments demonstrate the effectiveness of our approach.

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Section: A Previous Workmentioning

confidence: 99%