Tag Archives: Rao-blackwellization

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.

Rao-Blackwellized Particle Filter SLAM with grid maps in which particles do not contain the whole map but only a part

H. Jo, H. M. Cho, S. Jo and E. Kim, Efficient Grid-Based Rao–Blackwellized Particle Filter SLAM With Interparticle Map Sharing, IEEE/ASME Transactions on Mechatronics, vol. 23, no. 2, pp. 714-724, DOI: 10.1109/TMECH.2018.2795252.

In this paper, we propose a novel and efficient grid-based Rao-Blackwellized particle filter simultaneous localization and mapping (RBPF-SLAM) with interparticle map shaping (IPMS). The proposed method aims at saving the computational memory in the grid-based RBPF-SLAM while maintaining the mapping accuracy. Unlike conventional RBPF-SLAM in which each particle has its own map of the whole environment, each particle has only a small map of the nearby environment called an individual map in the proposed method. Instead, the map of the remaining large environment is shared by the particles. The part shared by the particles is called a base map. If the individual small maps become reliable enough to trust, they are merged with the base map. To determine when and which part of an individual map should be merged with the base map, we propose two map sharing criteria. Finally, the proposed IPMS RBPF-SLAM is applied to the real-world datasets and benchmark datasets. The experimental results show that our method outperforms conventional methods in terms of map accuracy versus memory consumption.

A brief general explanation of Rao-Blacwellization and a new way of applying it to reduce the variance of a point estimation in a sequential bayesian setting

Petetin, Y.; Desbouvries, F., Bayesian Conditional Monte Carlo Algorithms for Nonlinear Time-Series State Estimation, Signal Processing, IEEE Transactions on , vol.63, no.14, pp.3586,3598, DOI: 10.1109/TSP.2015.2423251.

Bayesian filtering aims at estimating sequentially a hidden process from an observed one. In particular, sequential Monte Carlo (SMC) techniques propagate in time weighted trajectories which represent the posterior probability density function (pdf) of the hidden process given the available observations. On the other hand, conditional Monte Carlo (CMC) is a variance reduction technique which replaces the estimator of a moment of interest by its conditional expectation given another variable. In this paper, we show that up to some adaptations, one can make use of the time recursive nature of SMC algorithms in order to propose natural temporal CMC estimators of some point estimates of the hidden process, which outperform the associated crude Monte Carlo (MC) estimator whatever the number of samples. We next show that our Bayesian CMC estimators can be computed exactly, or approximated efficiently, in some hidden Markov chain (HMC) models; in some jump Markov state-space systems (JMSS); as well as in multitarget filtering. Finally our algorithms are validated via simulations.