LiDAR directly acquires 3D point measurements of physical surfaces, making it well-suited for interpreting objects in tasks such as object reconstruction and semantic mapping. However, each object is represented by a sparse point set, and the observations are inherently partial due to self-occlusion, which poses significant challenges for object registration. The uncertainty of the modeled surface depends on the local point density, and outliers from non-overlapping areas are often involved in the registration process. To address these issues, we propose a stochastic object registration method that models surface uncertainty using a Gaussian process and an overlap determination scheme based on object detector outputs. Both the overlap determination scheme and the stochastic object registration method are verified through Monte Carlo simulations on numerous real-world urban object point clouds, demonstrating performance improvements of 52% and 12%, respectively, compared to the baseline methods. To validate the full pipeline in real-world conditions, the proposed registration method is integrated with PointPillars, achieving an 8% accuracy improvement over the baseline method and demonstrating a 98.8% convergence ratio in LiDAR-based vehicle registration.

In the presence of fast motion, point clouds obtained from mechanical spinning LiDAR can be easily distorted due to the slow scanning speed of the LiDAR. Existing LiDAR-only odometry algorithms generally ignore this distortion or compensate by linearly interpolating the estimated relative motion between scans. However, when there are abrupt and nonlinear motion changes, the linear interpolation method poorly compensates for the distortions, which can cause significant drift in motion estimates. In this work, we present a LiDAR-only odometry algorithm that estimates motion by slicing LiDAR scans into shorter times to compensate more agilely for point cloud distortions. Observations from only one small scan slice inevitably lack spatial uniqueness, so the multimodal problem needs to be addressed. For LiDAR-only odometry with small scan slices, we introduce the ensemble Kalman filter, a kind of Monte Carlo-based Bayesian filter. The proposed method makes it possible to perform odometry with only a very narrow field of view (FoV), and the robustness to point cloud distortion is improved. We demonstrate the effectiveness of the proposed method through Monte Carlo simulations and several tests with fast-moving scenarios. The experimental results prove the possibility of odometry with a very narrow FoV of down to 10 degrees and robustness against motion distortion.

In this article, we propose a visual-inertial navigation system that directly minimizes a photometric error without an explicit data-association. We focus on the photometric error parametrized by pose and structure parameters that is highly nonconvex due to the nonlinearity of image intensity. The key idea is to introduce anoptimal intensity gradientthat accounts for a projective uncertainty of a pixel. Ensembles sampled from the state uncertainty contribute to the proposed gradient and yield a correct update direction even in a bad initialization point. We present two sets of experiments to demonstrate the strengths of our framework. First, a thorough Monte Carlo simulation in a virtual trajectory is designed to reveal robustness to large initial uncertainty. Second, we show that the proposed framework can achieve superior estimation accuracy with efficient computation time over state-of-the-art visual-inertial fusion methods in a real-world UAV flight test, where most scenes are composed of a featureless floor.