Tag Archives: Scan Matching

A nice hybridization of RBPF, high-frequency scan matching and topological maps to perform SLAM, with an also nice state-of-the-art

Aristeidis G. Thallas, Emmanouil G. Tsardoulias, Loukas Petrou, Topological Based Scan Matching – Odometry Posterior Sampling in RBPF Under Kinematic Model Failures, Journal of Intelligent & Robotic Systems, September 2018, Volume 91, Issue 3–4, pp 543–568, DOI: 10.1007/s10846-017-0730-3.

Rao-Blackwellized Particle Filters (RBPF) have been utilized to provide a solution to the SLAM problem. One of the main factors that cause RBPF failure is the potential particle impoverishment. Another popular approach to the SLAM problem are Scan Matching methods, whose good results require environments with lots of information, however fail in the lack thereof. To face these issues, in the current work techniques are presented to combine Rao-Blackwellized particle filters with a scan matching algorithm (CRSM SLAM). The particle filter maintains the correct hypothesis in environments lacking features and CRSM is employed in feature-rich environments while simultaneously reduces the particle filter dispersion. Since CRSM’s good performance is based on its high iteration frequency, a multi-threaded combination is presented which allows CRSM to operate while RBPF updates its particles. Additionally, a novel method utilizing topological information is proposed, in order to reduce the number of particle filter resamplings. Finally, we present methods to address anomalous situations where scan matching can not be performed and the vehicle displays behaviors not modeled by the kinematic model, causing the whole method to collapse. Numerous experiments are conducted to support the aforementioned methods’ advantages.

Combination of several mobile robot localization methods in order to achieve high accuracy in industrial environments, with interesting figures for current localization accuracy achievable by standard solutions

Goran Vasiljevi, Damjan Mikli, Ivica Draganjac, Zdenko Kovai, Paolo Lista, High-accuracy vehicle localization for autonomous warehousing, Robotics and Computer-Integrated Manufacturing, Volume 42, December 2016, Pages 1-16, ISSN 0736-5845, DOI: 10.1016/j.rcim.2016.05.001.

The research presented in this paper aims to bridge the gap between the latest scientific advances in autonomous vehicle localization and the industrial state of the art in autonomous warehousing. Notwithstanding great scientific progress in the past decades, industrial autonomous warehousing systems still rely on external infrastructure for obtaining their precise location. This approach increases warehouse installation costs and decreases system reliability, as it is sensitive to measurement outliers and the external localization infrastructure can get dirty or damaged. Several approaches, well studied in scientific literature, are capable of determining vehicle position based only on information provided by on board sensors, most commonly wheel encoders and laser scanners. However, scientific results published to date either do not provide sufficient accuracy for industrial applications, or have not been extensively tested in realistic, industrial-like operating conditions. In this paper, we combine several well established algorithms into a high-precision localization pipeline, capable of computing the pose of an autonomous forklift to sub-centimeter precision. The algorithms use only odometry information from wheel encoders and range readings from an on board laser scanner. The effectiveness of the proposed solution is evaluated by an extensive experiment that lasted for several days, and was performed in a realistic industrial-like environment.