Real-time Pedestrian Localization and State Estimation Using Moving Horizon Estimation

In this work, we propose a constrained moving horizon state estimation approach to estimate a pedestrian's states in 3D with respect to a global stationary frame including position, velocity, and acceleration that are robust to intermittently noisy or absent sensor measurements. Utilizing a computationally light-weight fusion of a Deep Neural Network based 2D pedestrian detection algorithm and projected LIDAR depth measurements, the approach produces the required measurements relative to the vehicle frame and combines them with the rotation and translation information obtained via odometry. The performance of the proposed approach is experimentally verified on our dataset featuring urban pedestrian crossings, with and without ego vehicle motion.