Optimal Target Shape for LiDAR Pose Estimation

TL;DR

This paper proposes an optimized target shape for LiDAR-based pose estimation that reduces ambiguity and improves accuracy in sparse point cloud data, validated through simulation and real-world experiments.

Contribution

It introduces a novel target shape design that enhances LiDAR pose estimation accuracy by reducing ambiguity and quantization effects, along with a global estimation method leveraging target geometry.

Findings

Achieves centimeter-level translation accuracy at 30 meters distance.

Attains a few degrees of rotational error with the optimal shape.

Validated results with simulation and motion capture system.

Abstract

Targets are essential in problems such as object tracking in cluttered or textureless environments, camera (and multi-sensor) calibration tasks, and simultaneous localization and mapping (SLAM). Target shapes for these tasks typically are symmetric (square, rectangular, or circular) and work well for structured, dense sensor data such as pixel arrays (i.e., image). However, symmetric shapes lead to pose ambiguity when using sparse sensor data such as LiDAR point clouds and suffer from the quantization uncertainty of the LiDAR. This paper introduces the concept of optimizing target shape to remove pose ambiguity for LiDAR point clouds. A target is designed to induce large gradients at edge points under rotation and translation relative to the LiDAR to ameliorate the quantization uncertainty associated with point cloud sparseness. Moreover, given a target shape, we present a means that leverages the target's geometry to estimate the target's vertices while globally estimating the pose. Both the simulation and the experimental results (verified by a motion capture system) confirm that by using the optimal shape and the global solver, we achieve centimeter error in translation and a few degrees in rotation even when a partially illuminated target is placed 30 meters away. All the implementations and datasets are available at https://github.com/UMich-BipedLab/optimal_shape_global_pose_estimation.