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Accurate pose and trajectory estimates, are necessary components of autonomous robot navigation system. A wide variety of Simultaneous Localization and Mapping (SLAM) and localization algorithms have been developed by the robotics community to cater to this requirement. Some of the sensor fusion algorithms employed by SLAM and localization algorithms include the particle filter, Gaussian Particle Filter, the Extended Kalman Filter, the Unscented Kalman Filter, and the Central Difference Kalman Filter. To guarantee a rapid convergence of the state estimate to the ground truth, the prediction density of the sensor fusion algorithm must be as close to the true vehicle prediction density as possible. This paper presents a Kolmogorov–Smirnov statistic-based method to compare the prediction densities of the algorithms listed above. The algorithms are compared using simulations of noisy inputs provided to an autonomous robotic vehicle, and the obtained results are analyzed. The results are then validated using data obtained from a robot moving in controlled trajectories similar to the simulations.

This work introduces a new complete Simultaneous Localization and Mapping (SLAM) framework that uses an enriched representation of the world based on sensor fusion and is able to simultaneously provide an accurate localization of the vehicle. A method to create an Evidential grid representation from two very different sensors, laser scanner and stereo camera, allows a better handling of the dynamic aspects of the urban environment and a proper management of errors to create a more reliable map, thus having a more precise localization. A life-long layer with high level states is presented, it maintains a global map of the entire vehicle’s trajectory and distinguishes between static and dynamic obstacles. Finally, we propose a method that at each current map creation estimates the vehicle’s position by a grid matching algorithm based on image registration techniques. Results on a real road dataset show that the environment mapping data can be improved by adding relevant information that could be missed without the proposed approach. Moreover, the proposed localization method is able to reduce the drift and improve the localization compared to other methods using similar configurations.

In this paper, we propose a method to address the problem of scale uncertainty in monocular visual odometry (VO), which includes scale ambiguity and scale drift, using distance measurements from a single ultra-wideband (UWB) anchor. A variant of Levenberg–Marquardt (LM) nonlinear least squares regression method is proposed to rectify unscaled position data from monocular odometry with 1D point-to-point distance measurements. As a loosely-coupled approach, our method is flexible in that each input block can be replaced with one’s preferred choices for monocular odometry/SLAM algorithm and UWB sensor. Furthermore, we do not require the location of the UWB anchor as prior knowledge and will estimate both scale and anchor location simultaneously. However, it is noted that a good initial guess for anchor position can result in more accurate scale estimation. The performance of our method is compared with state-of-the-art on both public datasets and real-life experiments.

The combination of data from multiple sensors, also known as sensor fusion or data fusion, is a key aspect in the design of autonomous robots. In particular, algorithms able to accommodate sensor fusion techniques enable increased accuracy, and are more resilient against the malfunction of individual sensors. The development of algorithms for autonomous navigation, mapping and localization have seen big advancements over the past two decades. Nonetheless, challenges remain in developing robust solutions for accurate localization in dense urban environments, where the so-called last-mile delivery occurs. In these scenarios, local motion estimation is combined with the matching of real-time data with a detailed pre-built map. In this paper, we utilize data gathered with an autonomous delivery robot to compare different sensor fusion techniques and evaluate which are the algorithms providing the highest accuracy depending on the environment. The techniques we analyze and propose in this paper utilize 3D lidar data, inertial data, GNSS data and wheel encoder readings. We show how lidar scan matching combined with other sensor data can be used to increase the accuracy of the robot localization and, in consequence, its navigation. Moreover, we propose a strategy to reduce the impact on navigation performance when a change in the environment renders map data invalid or part of the available map is corrupted.

Intelligent unmanned systems have important applications, such as pesticide-spraying in agriculture, robot-based warehouse management systems, and missile-firing drones. The underlying assumption behind all autonomy is that the agent knows its relative position or egomotion with respect to some reference or scene. There exist thousands of localization systems in the literature. These localization systems use various combinations of sensors and algorithms, such as visual/visual-inertial SLAM, to achieve robust localization. The majority of the methods use one or more sensors from LIDAR, camera, IMU, UWB, GPS, compass, tracking system, etc. This survey presents a systematic review and analysis of published algorithms and techniques chronologically, and we introduce various highly impactful works. We provide insightful investigation and taxonomy on sensory data forming principle, feature association principle, egomotion estimation formation, and fusion model for each type of system. At last, some open problems and directions for future research are also included. We aim to survey the literature comprehensively to provide a complete understanding of localization methodologies, performance, advantages and limitations, and evaluations of various methods, shedding some light for future research.