# Efficient and Consistent Bundle Adjustment on Lidar Point Clouds

arXiv: https://arxiv.org/abs/2209.08854 · HTML: https://mlsanity.com/paper/2209.08854/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2022.

## Built on by (most cited first, 14 of 14)

- [NaVid: Video-based VLM Plans the Next Step for Vision-and-Language Navigation](https://mlsanity.com/paper/2402.15852/) (2024)
- [PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency](https://mlsanity.com/paper/2401.09101/) (2024)
- [GlobalPointer: Large-Scale Plane Adjustment with Bi-Convex Relaxation](https://mlsanity.com/paper/2407.13537/) (2024)
- [OmniColor: A Global Camera Pose Optimization Approach of LiDAR-360Camera Fusion for Colorizing Point Clouds](https://mlsanity.com/paper/2404.04693/) (2024)
- [PSS-BA: LiDAR Bundle Adjustment with Progressive Spatial Smoothing](https://mlsanity.com/paper/2403.06124/) (2024)
- [VIRUS-NeRF -- Vision, InfraRed and UltraSonic based Neural Radiance Fields](https://mlsanity.com/paper/2403.09477/) (2024)
- [SGBA: Semantic Gaussian Mixture Model-Based LiDAR Bundle Adjustment](https://mlsanity.com/paper/2410.01618/) (2024)
- [Correspondence-Free Multiview Point Cloud Registration via Depth-Guided Joint Optimisation](https://mlsanity.com/paper/2506.18922/) (2025)
- [An Efficient Plane Extraction Approach for Bundle Adjustment on LiDAR Point clouds](https://mlsanity.com/paper/2305.00287/) (2023)
- [Robust Second-order LiDAR Bundle Adjustment Algorithm Using Mean Squared Group Metric](https://mlsanity.com/paper/2409.01856/) (2024)
- [Micro-Structures Graph-Based Point Cloud Registration for Balancing Efficiency and Accuracy](https://mlsanity.com/paper/2410.21857/) (2024)
- [CSMapping: Scalable Crowdsourced Semantic Mapping and Topology Inference for Autonomous Driving](https://mlsanity.com/paper/2512.03510/) (2025)
- [Accelerating Large-scale Bundle Adjustment for LiDAR Mapping via Parallel Computing](https://mlsanity.com/paper/2608.14266/) (2026)
- [Free-Init: Scan-Free, Motion-Free, and Correspondence-Free Initialization for Doppler LiDAR-Inertial Systems](https://mlsanity.com/paper/2609.29375/) (2026)

## Abstract (arXiv)

Bundle Adjustment (BA) refers to the problem of simultaneous determination of sensor poses and scene geometry, which is a fundamental problem in robot vision. This paper presents an efficient and consistent bundle adjustment method for lidar sensors. The method employs edge and plane features to represent the scene geometry, and directly minimizes the natural Euclidean distance from each raw point to the respective geometry feature. A nice property of this formulation is that the geometry features can be analytically solved, drastically reducing the dimension of the numerical optimization. To represent and solve the resultant optimization problem more efficiently, this paper then proposes a novel concept it point clusters, which encodes all raw points associated to the same feature by a compact set of parameters, the it point cluster coordinates. We derive the closed-form derivatives, up to the second order, of the BA optimization based on the point cluster coordinates and show their theoretical properties such as the null spaces and sparsity. Based on these theoretical results, this paper develops an efficient second-order BA solver. Besides estimating the lidar poses, the solver also exploits the second order information to estimate the pose uncertainty caused by measurement noises, leading to consistent estimates of lidar poses. Moreover, thanks to the use of point cluster, the developed solver fundamentally avoids the enumeration of each raw point (which is very time-consuming due to the large number) in all steps of the optimization: cost evaluation, derivatives evaluation and uncertainty evaluation. The implementation of our method is open sourced to benefit the robotics community and beyond.