2 research outputs found
SimuGAN: Unsupervised forward modeling and optimal design of a LIDAR Camera
Energy-saving LIDAR camera for short distances estimates an object's distance
using temporally intensity-coded laser light pulses and calculates the maximum
correlation with the back-scattered pulse.
Though on low power, the backs-scattered pulse is noisy and unstable, which
leads to inaccurate and unreliable depth estimation.
To address this problem, we use GANs (Generative Adversarial Networks), which
are two neural networks that can learn complicated class distributions through
an adversarial process. We learn the LIDAR camera's hidden properties and
behavior, creating a novel, fully unsupervised forward model that simulates the
camera. Then, we use the model's differentiability to explore the camera
parameter space and optimize those parameters in terms of depth, accuracy, and
stability. To achieve this goal, we also propose a new custom loss function
designated to the back-scattered code distribution's weaknesses and its
circular behavior. The results are demonstrated on both synthetic and real
data
Improvements to Target-Based 3D LiDAR to Camera Calibration
The homogeneous transformation between a LiDAR and monocular camera is
required for sensor fusion tasks, such as SLAM. While determining such a
transformation is not considered glamorous in any sense of the word, it is
nonetheless crucial for many modern autonomous systems. Indeed, an error of a
few degrees in rotation or a few percent in translation can lead to 20 cm
translation errors at a distance of 5 m when overlaying a LiDAR image on a
camera image. The biggest impediments to determining the transformation
accurately are the relative sparsity of LiDAR point clouds and systematic
errors in their distance measurements. This paper proposes (1) the use of
targets of known dimension and geometry to ameliorate target pose estimation in
face of the quantization and systematic errors inherent in a LiDAR image of a
target, and (2) a fitting method for the LiDAR to monocular camera
transformation that fundamentally assumes the camera image data is the most
accurate information in one's possession