Prior Distribution Refinement for Reference Trajectory Estimation With the Monte Carlo-Based Localization Algorithm

Abstract

A robot localization problem demands a fair comparison of the positioning algorithms. A reference trajectory of the robot's movement is needed to estimate errors and evaluate a quality of the localization. In this article, we propose the Prior Distribution Refinement method for generating a reference trajectory of a mobile robot with the Monte Carlo-based localization system. The proposed approach can be applied for both indoor and outdoor environments of an arbitrary size without the need for expensive position tracking sensors or intervention in the testing infrastructure. The reference trajectory is generated by running the algorithm over a so-called Particles' Transition Graph, obtained from a resampling stage of Monte Carlo localization. The prior distribution of particles is then refined by forward-backward propagation through the graph and exploring the connections between particles. The Viterbi algorithm is applied afterwards to generate a reference trajectory based on refined particles' distribution. We demonstrate that such an approach is capable of generating accurate estimates of a mobile robot's position and orientation with the only requirement of moderate quality of localization system being used as a core algorithm for iterative optimization.

Efficient single- and multi-DNN inference using TensorRT framework

“…Nowadays, artificial intelligence (AI) video analytics systems are used extensively in multiple real-life applications, including self-driving cars [1,2], surveillance [3], medicine [4], document recognition [5] and agriculture [6]. In the pursuit of high recognition accuracy, the core components of these systems -deep neural networks (DNNs) -have become more and more computationally complex [7].…”