# Greedy-Based Feature Selection for Efficient LiDAR SLAM

arXiv: https://arxiv.org/abs/2103.13090 · HTML: https://mlsanity.com/paper/2103.13090/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 49 references that are in the map.
- First posted on arXiv 24 Mar 2021.

## Builds on (most cited first, 19 of 19)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://mlsanity.com/paper/1812.04244/) (2018)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset](https://mlsanity.com/paper/1909.01300/) (2019)
- [Lazier Than Lazy Greedy](https://mlsanity.com/paper/1409.7938/) (2014)
- [SampleNet: Differentiable Point Cloud Sampling](https://mlsanity.com/paper/1912.03663/) (2019)
- [A Polynomial-time Solution for Robust Registration with Extreme Outlier Rates](https://mlsanity.com/paper/1903.08588/) (2019)
- [Tightly Coupled 3D Lidar Inertial Odometry and Mapping](https://mlsanity.com/paper/1904.06993/) (2019)
- [BALM: Bundle Adjustment for Lidar Mapping](https://mlsanity.com/paper/2010.08215/) (2020)
- [SLOAM: Semantic Lidar Odometry and Mapping for Forest Inventory](https://mlsanity.com/paper/1912.12726/) (2019)
- [Robust Odometry and Mapping for Multi-LiDAR Systems with Online Extrinsic Calibration](https://mlsanity.com/paper/2010.14294/) (2020)
- [On Submodularity and Controllability in Complex Dynamical Networks](https://mlsanity.com/paper/1404.7665/) (2014)
- [Attention and Anticipation in Fast Visual-Inertial Navigation](https://mlsanity.com/paper/1610.03344/) (2016)
- [Good Feature Matching: Towards Accurate, Robust VO/VSLAM with Low Latency](https://mlsanity.com/paper/2001.00714/) (2020)
- [A decentralized framework for simultaneous calibration, localization and mapping with multiple LiDARs](https://mlsanity.com/paper/2007.01483/) (2020)
- [SLAMBench2: Multi-Objective Head-to-Head Benchmarking for Visual SLAM](https://mlsanity.com/paper/1808.06820/) (2018)
- [GMMLoc: Structure Consistent Visual Localization with Gaussian Mixture Models](https://mlsanity.com/paper/2006.13670/) (2020)
- [Good Graph to Optimize: Cost-Effective, Budget-Aware Bundle Adjustment in Visual SLAM](https://mlsanity.com/paper/2008.10123/) (2020)

## Built on by (most cited first, 9 of 9)

- [Scal3R: Scalable Test-Time Training for Large-Scale 3D Reconstruction](https://mlsanity.com/paper/2604.08542/) (2026)
- [MMFN: Multi-Modal-Fusion-Net for End-to-End Driving](https://mlsanity.com/paper/2207.00186/) (2022)
- [Prompting Multi-Modal Tokens to Enhance End-to-End Autonomous Driving Imitation Learning with LLMs](https://mlsanity.com/paper/2404.04869/) (2024)
- [LiteVLoc: Map-Lite Visual Localization for Image Goal Navigation](https://mlsanity.com/paper/2410.04419/) (2024)
- [MaskFuser: Masked Fusion of Joint Multi-Modal Tokenization for End-to-End Autonomous Driving](https://mlsanity.com/paper/2405.07573/) (2024)
- [Network Generalization Prediction for Safety Critical Tasks in Novel Operating Domains](https://mlsanity.com/paper/2108.07399/) (2021)
- [Perception-and-Energy-aware Motion Planning for UAV using Learning-based Model under Heteroscedastic Uncertainty](https://mlsanity.com/paper/2309.14272/) (2023)
- [RMS: Redundancy-Minimizing Point Cloud Sampling for Real-Time Pose Estimation](https://mlsanity.com/paper/2312.07337/) (2023)
- [DCReg: Decoupled Characterization for Efficient Degenerate LiDAR Registration](https://mlsanity.com/paper/2509.06285/) (2025)

## Abstract (arXiv)

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 stochastic-greedy 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.