Siwei Guo

There exist many accurate and efficient methods exist that address the mapping problem but most assume that the occupancy states of different elements in the map are statistically independent. Correlation is important not only for improved accuracy but also for quantifying uncertainty in the map and for planning autonomous mapping trajectories . Recent work proposes Gaussian Process to capture covariance information and enable resolution-free occupancy estimation. The drawback of techniques based on GP regression (or classification) is that the computation complexity scales cubically with the length of the measurement history.

 

Our goal is to speed up the process such that online updating is possible for large scale map without compromising too much on the precision while capturing the correlation of the occupancy of map elements. We proposed two modifications on gaussian process approach.

First, in stead of gaussian process regression, we solve the classification problem by keeping the occupancy grid representation of the map and model the binary nature of occupancy measurements (why would we need such high resolution). Second, we use information filter to replace gaussian process which is easier to compute . In order to still keep the correlation information, we apply the kernel function used in gaussian process which captures the spatial correlation between points as covariance matrix for prior distribution. The object is to prove that the error between the estimates provided by our method and those provided by GP classification is negligible.

​

Project under Professor Nikolay Atanasov in ECE Department at UCSD