Real-time Scan-Matching Using L0-norm Minimization Under Dynamic Crowded Environments

We propose real-time scan-matching based on L0norm minimization under dynamic crowded environment. The prior scan-matching methods are based on L2-norm minimization, because the measurement noise follows the normal distribution in static environments. This assumption is unfortunately broken in dynamic crowded environments. We propose to use the idea of Locality Sensitive Hashing (LSH) to accelerate the L0-norm minimization, which usually is a time-consuming process. The LSH customized for our issue reduces the calculation time even in the worst cases. The experimental results demonstrate the effectiveness of the proposed method compared with standard L2-norm minimization and its robust version with M-estimator.