Organizations: Division of Robotics and Perception and Learning, KTH Royal Institute of Technology, Stockholm, Sweden · Department of Mechanical Engineering, the University of Hong Kong · School of Computing and Data Science, the University of Hong Kong
LiDAR bundle adjustment is widely utilized in mapping to construct globally consistent point cloud maps. In this paper, we propose the first fully parallel computing framework to accelerate LiDAR bundle adjustment for large-scale mapping, incorporating three key techniques. First, we design an adaptive, asynchronous data loading strategy to efficiently process large-scale point cloud datasets on memory-constrained GPUs. Secondly, we present a novel bottom-up voxelization method for extracting planar features, enabling fully parallelized pre-processing. Thirdly, we build upon a majorization-minimization formulation to accelerate compute-intensive tasks in the optimization via parallel computation, including the computation of residuals, Jacobian and Hessian matrices, and a parallel increment solver. To support our design, we provide both theoretical and experimental analysis of the time complexity of our approach. Extensive benchmarking on large-scale public datasets across various computational platforms validates the robustness and adaptability of our approach, achieving up to a tenfold improvement in computational efficiency while preserving mapping accuracy comparable to state-of-the-art methods. To benefit future research, the implementation code is available on GitHub.
Figures & tables
Fig. 1: Left: Parallel Computation Flow. Boxes with dotted lines indicate sequential computations performed on the CPU, whereas those with solid lines denote parallel computations executed on the GPU. The red boxes represent the preprocessing process, as described in Section IV-B , while the blue boxes indicate the optimization process, as detailed in Section IV-C . The green box with dotted lines denotes the Levenberg–Marquardt (LM) algorithm serving as the optimizer. Right: Illustration of Adaptive Asynchronous Data Loader
Dataset
Sequence
Total Time (s)
RMSE (m)
HBA
BALM3
Ours
Speed 2 ↑
HBA
BALM3
Ours
Δerr 3 ↓
L=3
L=1 ∗
L=3
L=1 ∗
HeLiPR
Roundabout_Avia01
214.63
152.04
22.15
17.53
× 6.86
2.085
1.722
1.583
1.968
-0.1383
Roundabout_Avia02
197.99
61.42
10.74
1.56
× 5.72
1.384
1.471
1.444
2.473
0.0594
Roundabout_Avia03
175.75
185.43
18.59
12.92
× 9.97
2.099
1.870
2.010
2.306
0.1399
Bridge_Avia01
236.73
52.29
6.73
4.79
× 7.77
11.129
11.186
11.160
11.228
0.0311
TABLE I: Benchmark Comparison in Efficiency and Accuracy
Fig. 2: Time Breakdown Comparison of Our Method and BALM3 for the HeLiPR Dataset. The bars represent preprocessing, evaluation, and solver times for six sequences (R01 to R03 and B01 to B03, corresponding to Roundabout_Avia01 to Roundabout_Avia03 and Bridge_Avia01 to Bridge_Avia03 , respectively). Blue bars for our method are shifted to align with the red bars for BALM3 for a clearer visual comparison of their values.
Recent research has achieved remarkable novel view rendering and scene reconstruction results with Neural Radiance Field (NeRF), including extensions to the LiDAR modality. Few studies have, however, explored the key design differences between RGB NeRFs and LiDAR NeRFs, particularly considering their underlying working principles. In this work, we provide both theoretical and empirical evidence suggesting that the density of volume sampling plays a significant role in LiDAR NeRF. Based on this finding, we propose a novel Neural LiDAR Bundle Adjustment (NeLD-BA) algorithm, which is tailored using efficient volume sampling of LiDAR rays for joint optimization of LiDAR map and poses. Extensive experiments are performed using the Newer College and FusionPortable datasets to demonstrate the proposed NeLD-BA's state-of-the-art performance in multi-view point cloud registration and 3D mapping. We will open-source our code for the community.
Real-time LiDAR-visual-inertial odometry and mapping is crucial for navigation and planning tasks in intelligent transportation systems. This study presents a pose-only bundle adjustment (PA) LiDAR-visual-inertial odometry (LVIO), named PA-LVIO, to meet the urgent need for real-time navigation and mapping. The proposed PA framework for LiDAR and visual measurements is highly accurate and efficient, and it can derive reliable frame-to-frame constraints within multiple frames. A marginalization-free and frame-to-map (F2M) LiDAR measurement model is integrated into the state estimator to eliminate odometry drifts. Meanwhile, an IMU-centric online spatial-temporal calibration is employed to obtain a pixel-wise LiDAR-camera alignment. With accurate estimated odometry and extrinsics, a high-quality and RGB-rendered point-cloud map can be built. Comprehensive experiments are conducted on both public and private datasets collected by wheeled robot, unmanned aerial vehicle (UAV), and handheld devices with 28 sequences and more than 50 km trajectories. Sufficient results demonstrate that the proposed PA-LVIO yields superior or comparable performance to state-of-the-art LVIO methods, in terms of the odometry accuracy and mapping quality. Besides, PA-LVIO can run in real-time on both the desktop PC and the onboard ARM computer. The codes and datasets are open sourced on GitHub (https://github.com/i2Nav-WHU/PA-LVIO) to benefit the community.
Hailiang Tang, Tisheng Zhang, Liqiang Wang +3
the GNSS Research Center, Wuhan University, Wuhan 430079, China · the Hubei Technology Innovation Center for Spatiotemporal Information and Positioning Navigation, Wuhan 430079, China · the Hubei Luojia Laboratory, Wuhan 430079, China
In real-time Visual SLAM systems, local mapping must operate under strict latency constraints, as delays degrade map quality and increase the risk of tracking failure. GPU parallelization offers a promising way to reduce latency. However, parallelizing local mapping is challenging due to synchronized shared-state updates and the overhead of transferring large map data structures to the GPU. This paper presents TurboMap, a GPU-parallelized and CPU-optimized local mapping backend that holistically addresses these challenges. We restructure Map Point Creation to enable parallel Keypoint Correspondence Search on the GPU, redesign and parallelize Map Point Fusion, optimize Redundant Keyframe Culling on the CPU, and integrate a fast GPU-based Local Bundle Adjustment solver. To minimize data transfer and synchronization costs, we introduce persistent GPU-resident keyframe storage. Experiments on the EuRoC and TUM-VI datasets show average local mapping speedups of 1.3x and 1.6x, respectively, while preserving accuracy.
Parsa Hosseininejad, Kimia Khabiri, Shishir Gopinath +3