Detection of Misalignment Using Markov Random Field with Fully Connected Latent Variables in LiDAR-Based Localization
Naoki Akai, Takatsugu Hirayama, Hiroshi Murase · The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) · 2020
This paper presents a misalignment recognition method using a Markov random field with fully connected latent variables for LiDAR-based localization. The major difficulty for the misalignment recognition is that considering entire relation of sensor measurement in localization is impossible because it must be assumed that the sensor measurement is independent to one another. The presented method enables to consider the entire relation via the fully connected latent variables. This paper also presents a calculation method of localization failure probability based on the misalignment recognition. Experiments show that the presented method can exactly detect misalignment even though partial sensor measurement overlaps with a map and can exactly recognize success and failure localization results.