LLOL: Low-Latency Odometry for Spinning Lidars

TL;DR

This paper introduces LLOL, a low-latency odometry system for spinning lidars that processes data in real-time as it arrives, significantly reducing delay and increasing throughput compared to traditional methods.

Contribution

The paper presents a streaming-based odometry approach for spinning lidars that minimizes latency and improves processing speed over existing methods.

Findings

Achieves lower latency than sweep-based methods.

Provides a lightweight and high-throughput odometry system.

Open-source implementation available for community use.

Abstract

In this paper, we present a low-latency odometry system designed for spinning lidars. Many existing lidar odometry methods wait for an entire sweep from the lidar before processing the data. This introduces a large delay between the first laser firing and its pose estimate. To reduce this latency, we treat the spinning lidar as a streaming sensor and process packets as they arrive. This effectively distributes expensive operations across time, resulting in a very fast and lightweight system with much higher throughput and lower latency. Our open-source implementation is available at \url{https://github.com/versatran01/llol}.