A Random-Finite-Set Approach to Bayesian SLAM

By John Mullane, Ba-Ngu Vo, Martin David Adams, B. Vo. IEEE Transactions on robotics, 2011

304 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/2adc7ac3629dfe0ab18e2f8fbb8e92d9fa08dfaf

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Citations in the corpus

Labeled Random Finite Sets and Multi-Object Conjugate Priors (2013, background): “I. INTRODUCTION I NFERENCE in multi-object systems involves the simul-taneous estimation of the number of objects along with their states, and lies at the heart of two seemingly unrelated fields—target tracking and spatial statistics.”
Indoor Tracking: Theory, Methods, and Technologies (2015, background): “Common challenges associated to SLAM include wrong data association among observations and landmarks [69], [70] and the close-the-loop problem, which occurs when a landmark is reobserved after a large period.”
Convergent Communication, Sensing and Localization in 6G Systems: An Overview of Technologies, Opportunities and Challenges (2021, methodology): “The online methods vary from simple EKF-SLAM to sophisticated methods based on random ﬁnite set theory [75].”
The Labeled Multi-Bernoulli Filter (2014, background): “Multi-target tracking is an established subfield of signal processing with applications spanning many areas such as radar, sonar [1], computer vision [4], robotics [5], cell biology [6], vehicle environment perception [7], etc.”
Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter (2013, methodology): “au) multi-target estimation method with applications in sonar [7], computer vision [8], [9], [10], [11], field robotics [12], [1 3], [14], [15], [16] traffic monitoring [17], [18], [19], cell bi ology [20], [10], [21], sensor network and distributed estimatio n [22], [23], [24], [25] etc.”
An Efficient Implementation of the Generalized Labeled Multi-Bernoulli Filter (2016, methodology): “Although this approach does not produce tracks like the GLMB filter, it is still useful in applications such as mapping [15], [16] where the individual trajectories of the landmarks are not required.”
Multiple‐Robot Simultaneous Localization and Mapping: A Review (2016, methodology): “, 2014) (Lee, Clark, & Salvi, 2012) (Mullane et al., 2011) feature feature feature Pros: number of features and data association are estimated with the Bayesian filter Cons: higher time complexity than vector-based solutions”
Adaptive Target Birth Intensity for PHD and CPHD Filters (2012, methodology): “[6] Mullane, J., et al. A random finite set approach to Bayesian SLAM.”