LONER: LiDAR Only Neural Representations for Real-Time SLAM

Core Concepts

Proposing LONER, a real-time LiDAR SLAM algorithm using neural implicit scene representation.

Abstract

Introduces LONER, the first real-time LiDAR-only SLAM algorithm. Utilizes a novel information-theoretic loss function for real-time performance. Evaluates qualitatively and quantitatively on open-source datasets. Outperforms existing methods in trajectory estimation and map reconstruction. Demonstrates competitive performance with state-of-the-art LiDAR SLAM methods.

Stats

Existing implicit mapping methods for LiDAR show promising results in large-scale reconstruction. LONER uses LiDAR data to train an MLP to estimate a dense map in real-time. Proposed method evaluated qualitatively and quantitatively on two open-source datasets.

Quotes

"The proposed loss function converges faster and leads to more accurate geometry reconstruction." "LONER estimates trajectories competitively with state-of-art LiDAR SLAM methods."

Key Insights Distilled From

LONER

by Seth Isaacso... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2309.04937.pdf

Deeper Inquiries

How can adding RGB data improve the realism of reconstructions?

Adding RGB data to LiDAR SLAM algorithms can enhance the realism of reconstructions by providing color information that complements the geometric details captured by LiDAR sensors. RGB data can help in texture mapping, improving visual fidelity, and enabling more realistic renderings of scenes. By combining RGB and LiDAR data, it is possible to create more visually appealing and informative reconstructions that closely resemble real-world environments. Additionally, RGB data can aid in object recognition and scene understanding, leading to more accurate mapping results.

What are the implications of handling dynamic objects in highly dynamic environments?

Handling dynamic objects in highly dynamic environments poses several challenges for SLAM systems. Dynamic objects introduce uncertainty into the environment as their positions change over time, impacting localization accuracy and map consistency. In such scenarios, SLAM algorithms need robust mechanisms to differentiate between static and moving elements to prevent drift or errors in trajectory estimation. Failure to account for dynamic objects can lead to inaccuracies in mapping results and pose estimation.

How does the proposed JS loss function compare to other loss functions in terms of convergence speed?

The proposed Jensen-Shannon (JS) loss function offers faster convergence compared to other loss functions used for depth-supervised training in neural implicit representations like NeRFs. The JS loss dynamically adjusts margins based on similarity between goal distributions and predicted sample distributions along each ray during training. This adaptive margin strategy allows regions with unknown geometry to be learned efficiently while preserving previously learned information without forgetting it. In contrast, traditional losses like Line-of-Sight (LOS) use a fixed margin shared across all rays which may hinder learning new regions effectively due to uniform constraints on different parts of the map during training. Experimental results demonstrate that the JS loss converges faster when trained on simulated LiDAR scans from CARLA simulator compared to LOS losses with various decay rates or depth-only losses commonly used in similar frameworks like NICE-SLAM or iMAP.