Greedy-Based Feature Selection for Efficient LiDAR SLAM

Abstract

Modern LiDAR-SLAM (L-SLAM) systems have shown excellent results in large-scale, real-world scenarios. However, they commonly have a high latency due to the expensive data association and nonlinear optimization. This paper demonstrates that actively selecting a subset of features significantly improves both the accuracy and efficiency of an L-SLAM system. We formulate the feature selection as a combinatorial optimization problem under a cardinality constraint to preserve the information matrix's spectral attributes. The stochasticgreedy algorithm is applied to approximate the optimal results in real-time. To avoid ill-conditioned estimation, we also propose a general strategy to evaluate the environment's degeneracy and modify the feature number online. The proposed feature selector is integrated into a multi-LiDAR SLAM system. We validate this enhanced system with extensive experiments covering various scenarios on two sensor setups and computation platforms. We show that our approach exhibits low localization error and speedup compared to the state-of-the-art L-SLAM systems. To benefit the community, we have released the source code: https://ram-lab.com/file/site/m-loam.

A Robust Framework for Simultaneous Localization and Mapping with Multiple Non-Repetitive Scanning Lidars

“…As reported in [41], the approximation ratio of a greedy approach was proven to be 1 − 1/e, thus the lazier greedy reaches a (1 − 1/e − ) approximation guarantee in expectation of the optimal solution. Let f (•) be the non-negative monotone and submodular function, and set the size of the random set as s. Differently from the adaptive adjustment of M K with online degeneracy evaluation in [42], we set a constant ratio of all features M K = 0.1F K for time efficiency. Let G * K be the optimal set and G sub K the result of the lazier-greedy algorithm; G sub K achieves the approximation guarantee in expectation:…”

Network Generalization Prediction for Safety Critical Tasks in Novel Operating Domains

“…The Mutual Information [12] is often used in filter methods to measure the information between a given feature and the desired label [22]. As exhaustive feature selection search is typically intractable, greedy feature selection algorithms are often used [9], [21], [8]. Note, greedy feature selection is related to matching pursuit in the sparse approximation literature [20] and has applications in compressed sensing [2].…”