SLAMSpoof: Practical LiDAR Spoofing Attacks on Localization Systems Guided by Scan Matching Vulnerability Analysis

Accurate localization is essential for enabling modern full self-driving services. These services heavily rely on map-based traffic information to reduce uncertainties in recognizing lane shapes, traffic light locations, and traffic signs. Achieving this level of reliance on map information requires centimeter-level localization accuracy, which is currently only achievable with LiDAR sensors. However, LiDAR is known to be vulnerable to spoofing attacks that emit malicious lasers against LiDAR to overwrite its measurements. Once localization is compromised, the attack could lead the victim off roads or make them ignore traffic lights. Motivated by these serious safety implications, we design SLAMSpoof, the first practical LiDAR spoofing attack on localization systems for self-driving to assess the actual attack significance on autonomous vehicles. SLAMSpoof can effectively find the effective attack location based on our scan matching vulnerability score (SMVS), a point-wise metric representing the potential vulnerability to spoofing attacks. To evaluate the effectiveness of the attack, we conduct real-world experiments on ground vehicles and confirm its high capability in real-world scenarios, inducing position errors of $\geq$4.2 meters (more than typical lane width) for all 3 popular LiDAR-based localization algorithms. We finally discuss the potential countermeasures of this attack. Code is available at https://github.com/Keio-CSG/slamspoof

GLIM: 3D Range-Inertial Localization and Mapping with GPU-Accelerated Scan Matching Factors

This article presents GLIM, a 3D range-inertial localization and mapping framework with GPU-accelerated scan matching factors. The odometry estimation module of GLIM employs a combination of fixed-lag smoothing and keyframe-based point cloud matching that makes it possible to deal with a few seconds of completely degenerated range data while efficiently reducing trajectory estimation drift. It also incorporates multi-camera visual feature constraints in a tightly coupled way to further improve the stability and accuracy. The global trajectory optimization module directly minimizes the registration errors between submaps over the entire map. This approach enables us to accurately constrain the relative pose between submaps with a small overlap. Although both the odometry estimation and global trajectory optimization algorithms require much more computation than existing methods, we show that they can be run in real-time due to the careful design of the registration error evaluation algorithm and the entire system to fully leverage GPU parallel processing.

MegaParticles: Range-based 6-DoF Monte Carlo Localization with GPU-Accelerated Stein Particle Filter

This paper presents a 6-DoF range-based Monte Carlo localization method with a GPU-accelerated Stein particle filter. To update a massive amount of particles, we propose a Gauss-Newton-based Stein variational gradient descent (SVGD) with iterative neighbor particle search. This method uses SVGD to collectively update particle states with gradient and neighborhood information, which provides efficient particle sampling. For an efficient neighbor particle search, it uses locality sensitive hashing and iteratively updates the neighbor list of each particle over time. The neighbor list is then used to propagate the posterior probabilities of particles over the neighbor particle graph. The proposed method is capable of evaluating one million particles in real-time on a single GPU and enables robust pose initialization and re-localization without an initial pose estimate. In experiments, the proposed method showed an extreme robustness to complete sensor occlusion (i.e., kidnapping), and enabled pinpoint sensor localization without any prior information.

Tightly-Coupled LiDAR-IMU-Wheel Odometry with Online Calibration of a Kinematic Model for Skid-Steering Robots

Tunnels and long corridors are challenging environments for mobile robots because a LiDAR point cloud should degenerate in these environments. To tackle point cloud degeneration, this study presents a tightly-coupled LiDAR-IMU-wheel odometry algorithm with an online calibration for skid-steering robots. We propose a full linear wheel odometry factor, which not only serves as a motion constraint but also performs the online calibration of kinematic models for skid-steering robots. Despite the dynamically changing kinematic model (e.g., wheel radii changes caused by tire pressures) and terrain conditions, our method can address the model error via online calibration. Moreover, our method enables an accurate localization in cases of degenerated environments, such as long and straight corridors, by calibration while the LiDAR-IMU fusion sufficiently operates. Furthermore, we estimate the uncertainty (i.e., covariance matrix) of the wheel odometry online for creating a reasonable constraint. The proposed method is validated through three experiments. The first indoor experiment shows that the proposed method is robust in severe degeneracy cases (long corridors) and changes in the wheel radii. The second outdoor experiment demonstrates that our method accurately estimates the sensor trajectory despite being in rough outdoor terrain owing to online uncertainty estimation of wheel odometry. The third experiment shows the proposed online calibration enables robust odometry estimation in changing terrains.

Tightly Coupled Range Inertial Localization on a 3D Prior Map Based on Sliding Window Factor Graph Optimization

This paper presents a range inertial localization algorithm for a 3D prior map. The proposed algorithm tightly couples scan-to-scan and scan-to-map point cloud registration factors along with IMU factors on a sliding window factor graph. The tight coupling of the scan-to-scan and scan-to-map registration factors enables a smooth fusion of sensor ego-motion estimation and map-based trajectory correction that results in robust tracking of the sensor pose under severe point cloud degeneration and defective regions in a map. We also propose an initial sensor state estimation algorithm that robustly estimates the gravity direction and IMU state and helps perform global localization in 3- or 4-DoF for system initialization without prior position information. Experimental results show that the proposed method outperforms existing state-of-the-art methods in extremely severe situations where the point cloud data becomes degenerate, there are momentary sensor interruptions, or the sensor moves along the map boundary or into unmapped regions.

3D-BBS: Global Localization for 3D Point Cloud Scan Matching Using Branch-and-Bound Algorithm

This paper presents an accurate and fast 3D global localization method, 3D-BBS, that extends the existing branch-and-bound (BnB)-based 2D scan matching (BBS) algorithm. To reduce memory consumption, we utilize a sparse hash table for storing hierarchical 3D voxel maps. To improve the processing cost of BBS in 3D space, we propose an efficient roto-translational space branching and best-first search strategy. Furthermore, we devise a batched BnB algorithm to fully leverage GPU parallel processing. Through experiments in simulated and real environments, we demonstrated that the 3D-BBS enabled accurate global localization with only a 3D LiDAR scan and a 3D pre-built map. This method required only 878 msec on average to perform global localization and outperformed state-of-the-art feature-matching-based global localization methods in terms of accuracy and processing speed.

Exact Point Cloud Downsampling for Fast and Accurate Global Trajectory Optimization

This paper presents a point cloud downsampling algorithm for fast and accurate trajectory optimization based on global registration error minimization. The proposed algorithm selects a weighted subset of residuals of the input point cloud such that the subset yields exactly the same quadratic point cloud registration error function as that of the original point cloud at the evaluation point. This method accurately approximates the original registration error function with only a small subset of input points (29 residuals at a minimum). Experimental results using the KITTI dataset demonstrate that the proposed algorithm significantly reduces processing time (by 87\%) and memory consumption (by 99\%) for global registration error minimization while retaining accuracy.

Single-Shot Global Localization via Graph-Theoretic Correspondence Matching

This paper describes a method of global localization based on graph-theoretic association of instances between a query and the prior map. The proposed framework employs correspondence matching based on the maximum clique problem (MCP). The framework is potentially applicable to other map and/or query modalities thanks to the graph-based abstraction of the problem, while many of existing global localization methods rely on a query and the dataset in the same modality. We implement it with a semantically labeled 3D point cloud map, and a semantic segmentation image as a query. Leveraging the graph-theoretic framework, the proposed method realizes global localization exploiting only the map and the query. The method shows promising results on multiple large-scale simulated maps of urban scenes.

General, Single-shot, Target-less, and Automatic LiDAR-Camera Extrinsic Calibration Toolbox

This paper presents an open source LiDAR-camera calibration toolbox that is general to LiDAR and camera projection models, requires only one pairing of LiDAR and camera data without a calibration target, and is fully automatic. For automatic initial guess estimation, we employ the SuperGlue image matching pipeline to find 2D-3D correspondences between LiDAR and camera data and estimate the LiDAR-camera transformation via RANSAC. Given the initial guess, we refine the transformation estimate with direct LiDAR-camera registration based on the normalized information distance, a mutual information-based cross-modal distance metric. For a handy calibration process, we also present several assistance capabilities (e.g., dynamic LiDAR data integration and user interface for making 2D-3D correspondence manually). The experimental results show that the proposed toolbox enables calibration of any combination of spinning and non-repetitive scan LiDARs and pinhole and omnidirectional cameras, and shows better calibration accuracy and robustness than those of the state-of-the-art edge-alignment-based calibration method.

Generalized LOAM: LiDAR Odometry Estimation with Trainable Local Geometric Features

This paper presents a LiDAR odometry estimation framework called Generalized LOAM. Our proposed method is generalized in that it can seamlessly fuse various local geometric shapes around points to improve the position estimation accuracy compared to the conventional LiDAR odometry and mapping (LOAM) method. To utilize continuous geometric features for LiDAR odometry estimation, we incorporate tiny neural networks into a generalized iterative closest point (GICP) algorithm. These neural networks improve the data association metric and the matching cost function using local geometric features. Experiments with the KITTI benchmark demonstrate that our proposed method reduces relative trajectory errors compared to the other LiDAR odometry estimation methods.