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John J. Leonard

46 accepted papers

2026

Knowledge Optical to Sonar (KnOTS): Towards the Transfer of Knowledge of Underwater Object Detection from Optical to Forward-Looking Sonar Imagery

Caroline Keenan, Ella R. Wawrzynek, David Whelihan, Ivy Mahncke, John J. Leonard, Madeline D. Miller
ICRA 2026poster

We develop an approach to detect objects in forward-looking sonar (FLS) images using corresponding optical images and without the need for expert manual labeling of sonar images. Sonar sensing is more robust to disadvantageous underwater environmental conditions than optical sensing, but the scarcit…

Cited by 0Scholar ↗
2025

3DGS-CD: 3D Gaussian Splatting-Based Change Detection for Physical Object Rearrangement

Ziqi Lu, Jianbo Ye, John J. Leonard
RA-L 2025

We present 3DGS-CD, the first 3D Gaussian Splatting (3DGS)-based method for detecting physical object rearrangements in 3D scenes. Our approach estimates 3D object-level changes by comparing two sets of unaligned images taken at different times. Leveraging 3DGS's novel view rendering and EfficientSA

Cited by 19Source ↗code ↗Scholar ↗
2025

Computational Teaching for Driving via Multi-Task Imitation Learning

Deepak E. Gopinath, Xiongyi Cui, Jonathan A. DeCastro, Emily Sumner, Jean Costa, Hiroshi Yasuda +7
ICRA 2025

Learning motor skills for sports or performance driving is often done with professional instruction from expert human teachers, whose availability is limited. Our goal is to enable automated teaching via a learned model that interacts with the student similar to a human teacher. However, training su

Cited by 4Source ↗Scholar ↗
2025

SeaSplat: Representing Underwater Scenes with 3D Gaussian Splatting and a Physically Grounded Image Formation Model

Daniel Yang, John J. Leonard, Yogesh A. Girdhar
ICRA 2025

We introduce SeaSplat, a method to enable real-time rendering of underwater scenes leveraging recent advances in 3D radiance fields. Underwater scenes are challenging visual environments, as rendering through a medium such as water introduces both range and color dependent effects on image capture.

Cited by 38Source ↗code ↗Scholar ↗
2025

Semantic Enhancement for Object SLAM with Heterogeneous Multimodal Large Language Model Agents

Jungseok Hong, Ran Choi, John J. Leonard
IROS 2025

Object Simultaneous Localization and Mapping (SLAM) systems struggle to correctly associate semantically similar objects in close proximity, especially in cluttered indoor environments and when scenes change. We present Semantic Enhancement for Object SLAM (SEO-SLAM), a novel framework that enhances

Cited by 4Source ↗Scholar ↗
2024

Opti-Acoustic Semantic SLAM with Unknown Objects in Underwater Environments

Kurran Singh, Jungseok Hong, Nicholas R. Rypkema, John J. Leonard
IROS 2024poster

Despite recent advances in semantic Simultaneous Localization and Mapping (SLAM) for terrestrial and aerial applications, underwater semantic SLAM remains an open and largely unaddressed research problem due to the unique sensing modalities and the object classes found underwater. This paper present…

Cited by 4Source ↗Scholar ↗
2023

Data-Association-Free Landmark-based SLAM

Yihao Zhang, Odin A. Severinsen, John J. Leonard, Luca Carlone, Kasra Khosoussi
ICRA 2023poster

We study landmark-based SLAM with unknown data association: our robot navigates in a completely unknown environment and has to simultaneously reason over its own trajectory, the positions of an unknown number of landmarks in the environment, and potential data associations between measurements and l…

Cited by 8Source ↗Scholar ↗
2023

GAPSLAM: Blending Gaussian Approximation and Particle Filters for Real-Time Non-Gaussian SLAM

Qiangqiang Huang, John J. Leonard
IROS 2023poster

Inferring the posterior distribution in SLAM is critical for evaluating the uncertainty in localization and mapping, as well as supporting subsequent planning tasks aiming to reduce uncertainty for safe navigation. However, real-time full posterior inference techniques, such as Gaussian approximatio…

Cited by 1Source ↗code ↗Scholar ↗
2023

NeRF-SLAM: Real-Time Dense Monocular SLAM with Neural Radiance Fields

Antoni Rosinol, John J. Leonard, Luca Carlone
IROS 2023poster

We propose a novel geometric and photometric 3D mapping pipeline for accurate and real-time scene reconstruction from casually taken monocular images. To achieve this, we leverage recent advances in dense monocular SLAM and real-time hierarchical volumetric neural radiance fields. Our insight is tha…

Cited by 317Source ↗code ↗Scholar ↗
2023

Optimizing Fiducial Marker Placement for Improved Visual Localization

Qiangqiang Huang, Joseph DeGol, Victor Fragoso, Sudipta N. Sinha, John J. Leonard
RA-L 2023

Adding fiducial markers to a scene is a well-known strategy for making visual localization algorithms more robust. Traditionally, these marker locations are selected by humans who are familiar with visual localization techniques. This letter explores the problem of automatic marker placement within

Cited by 5Source ↗code ↗Scholar ↗
2023

SCORE: A Second-Order Conic Initialization for Range-Aided SLAM

Alan Papalia, Joseph Morales, Kevin J. Doherty, David M. Rosen, John J. Leonard
ICRA 2023poster

We present a novel initialization technique for the range-aided simultaneous localization and mapping (RA-SLAM) problem. In RA-SLAM we consider measurements of point-to-point distances in addition to measurements of rigid transformations to landmark or pose variables. Standard formulations of RA-SLA…

Cited by 9Source ↗code ↗Scholar ↗
2022

HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling

Xin Huang, Guy Rosman, Igor Gilitschenski, Ashkan Jasour, Stephen G. McGill, John J. Leonard +1
ICRA 2022poster

Modeling multi-modal high-level intent is important for ensuring diversity in trajectory prediction. Existing approaches explore the discrete nature of human intent before predicting continuous trajectories, to improve accuracy and support explainability. However, these approaches often assume the i…

Cited by 33Source ↗Scholar ↗
2022

Performance Guarantees for Spectral Initialization in Rotation Averaging and Pose-Graph SLAM

Kevin J. Doherty, David M. Rosen, John J. Leonard
ICRA 2022poster

In this work we present the first initialization methods equipped with explicit performance guarantees that are adapted to the pose-graph simultaneous localization and mapping (SLAM) and rotation averaging (RA) problems. SLAM and rotation averaging are typically formalized as large-scale nonconvex p…

Cited by 21Source ↗Scholar ↗
2022

PlaneSDF-Based Change Detection for Long-Term Dense Mapping

Jiahui Fu, Chengyuan Lin, Yuichi Taguchi, Andrea Cohen, Yifu Zhang, Stephen Mylabathula +1
RA-L 2022

The ability to process environment maps across multiple sessions is critical for robots operating over extended periods of time. Specifically, it is desirable for autonomous agents to detect changes amongst maps of different sessions so as to gain a conflict-free understanding of the current environ

Cited by 10Source ↗Scholar ↗
2022

Robust Change Detection Based on Neural Descriptor Fields

Jiahui Fu, Yilun Du, Kurran Singh, Joshua B. Tenenbaum, John J. Leonard
IROS 2022poster

The ability to reason about changes in the environment is crucial for robots operating over extended periods of time. Agents are expected to capture changes during operation so that actions can be followed to ensure a smooth progression of the working session. However, varying viewing angles and acc…

Cited by 10Source ↗code ↗Scholar ↗
2022

TIP: Task-Informed Motion Prediction for Intelligent Vehicles

Xin Huang, Guy Rosman, Ashkan Jasour, Stephen G. McGill, John J. Leonard, Brian C. Williams
IROS 2022poster

When predicting trajectories of road agents, motion predictors often approximate the future distribution by a limited number of samples. This constraint requires the predictors to generate samples that best support the task given task specifications. However, existing predictors are often optimized…

Cited by 15Source ↗Scholar ↗
2022

Trajectory Prediction with Linguistic Representations

Yen-Ling Kuo, Xin Huang, Andrei Barbu, Stephen G. McGill, Boris Katz, John J. Leonard +1
ICRA 2022poster

Language allows humans to build mental models that interpret what is happening around them resulting in more accurate long-term predictions. We present a novel trajectory prediction model that uses linguistic intermediate representations to forecast trajectories, and is trained using trajectory samp…

Cited by 22Source ↗Scholar ↗
2021

A Multi-Hypothesis Approach to Pose Ambiguity in Object-Based SLAM

Jiahui Fu, Qiangqiang Huang, Kevin Doherty, Yue Wang, John J. Leonard
IROS 2021poster

In object-based Simultaneous Localization and Mapping (SLAM), 6D object poses offer a compact representation of landmark geometry useful for downstream planning and manipulation tasks. However, measurement ambiguity then arises as objects may possess complete or partial object shape symmetries (e.g.…

Cited by 22Source ↗Scholar ↗
2021

Bootstrapped Self-Supervised Training with Monocular Video for Semantic Segmentation and Depth Estimation

Yihao Zhang, John J. Leonard
IROS 2021poster

For a robot deployed in the world, it is desirable to have the ability of autonomous learning to improve its initial pre-set knowledge. We formalize this as a bootstrapped self-supervised learning problem where a system is initially bootstrapped with supervised training on a labeled dataset and we l…

Cited by 4Source ↗Scholar ↗
2021

CARPAL: Confidence-Aware Intent Recognition for Parallel Autonomy

Xin Huang, Stephen G. McGill, Jonathan A. DeCastro, Luke Fletcher, John J. Leonard, Brian C. Williams +1
RA-L 2021

Predicting driver intentions is a difficult and crucial task for advanced driver assistance systems. Traditional confidence measures on predictions often ignore the way predicted trajectories affect downstream decisions for safe driving. In this letter, we propose a novel multi-task intent recogniti

Cited by 7Source ↗Scholar ↗
2021

Consensus-Informed Optimization Over Mixtures for Ambiguity-Aware Object SLAM

Ziqi Lu, Qiangqiang Huang, Kevin Doherty, John J. Leonard
IROS 2021poster

Building object-level maps can facilitate robot-environment interactions (e.g. planning and manipulation), but objects could often have multiple probable poses when viewed from a single vantage point, due to symmetry, occlusion or perceptual failures. A robust object-level simultaneous localization…

Cited by 11Source ↗Scholar ↗
2021

Lidar-Monocular Surface Reconstruction Using Line Segments

Victor Amblard, Timothy P. Osedach, Arnaud Croux, Andrew Speck, John J. Leonard
ICRA 2021poster

Structure from Motion (SfM) often fails to estimate accurate poses in environments that lack suitable visual features. In such cases, the quality of the final 3D mesh, which is contingent on the accuracy of those estimates, is reduced. One way to overcome this problem is to combine data from a monoc…

Cited by 14Source ↗Scholar ↗
2021

NF-iSAM: Incremental Smoothing and Mapping via Normalizing Flows

Qiangqiang Huang, Can Pu, Dehann Fourie, Kasra Khosoussi, Jonathan P. How, John J. Leonard
ICRA 2021poster

This paper presents a novel non-Gaussian inference algorithm, Normalizing Flow iSAM (NF-iSAM), for solving SLAM problems with non-Gaussian factors and/or non-linear measurement models. NF-iSAM exploits the expressive power of neural networks, and trains normalizing flows to draw samples from the joi…

Cited by 16Source ↗Scholar ↗
2020

DiversityGAN: Diversity-Aware Vehicle Motion Prediction via Latent Semantic Sampling

Xin Huang, Stephen G. McGill, Jonathan A. DeCastro, Luke Fletcher, John J. Leonard, Brian C. Williams +1
RA-L 2020

Vehicle trajectory prediction is crucial for autonomous driving and advanced driver assistant systems. While existing approaches may sample from a predicted distribution of vehicle trajectories, they lack the ability to explore it - a key ability for evaluating safety from a planning and verificatio

Cited by 80Source ↗Scholar ↗
2020

Probabilistic Data Association via Mixture Models for Robust Semantic SLAM

Kevin J. Doherty, David P. Baxter, Edward Schneeweiss, John J. Leonard
ICRA 2020poster

Modern robotic systems sense the environment geometrically, through sensors like cameras, lidar, and sonar, as well as semantically, often through visual models learned from data, such as object detectors. We aim to develop robots that can use all of these sources of information for reliable navigat…

Cited by 87Source ↗code ↗Scholar ↗
2019

Dense, Sonar-based Reconstruction of Underwater Scenes

Pedro V. Teixeira, Dehann Fourie, Michael Kaess, John J. Leonard
IROS 2019poster

Typically, the reconstruction problem is addressed in three independent steps: first, sensor processing techniques are used to filter and segment sensor data as required by the front end. Second, the front end builds the factor graph for the problem to obtain an accurate estimate of the robot’s full…

Cited by 21Source ↗Scholar ↗
2019

Probabilistic Risk Metrics for Navigating Occluded Intersections

Stephen G. McGill, Guy Rosman, Teddy Ort, Alyssa Pierson, Igor Gilitschenski, Brandon Araki +4
RA-L 2019

Among traffic accidents in the USA, 23% of fatal and 32% of non-fatal incidents occurred at intersections. For driver assistance systems, intersection navigation remains a difficult problem that is critically important to increasing driver safety. In this letter, we examine how to navigate an unsign

Cited by 36Source ↗Scholar ↗
2016

Underwater inspection using sonar-based volumetric submaps

Pedro V. Teixeira, Michael Kaess, Franz S. Hover, John J. Leonard
IROS 2016poster

We propose a submap-based technique for mapping of underwater structures with complex geometries. Our approach relies on the use of probabilistic volumetric techniques to create submaps from multibeam sonar scans, as these offer increased outlier robustness. Special attention is paid to the problem…

Cited by 78Source ↗Scholar ↗
2015

A convex relaxation for approximate global optimization in simultaneous localization and mapping

David M. Rosen, Charles DuHadway, John J. Leonard
ICRA 2015poster

Modern approaches to simultaneous localization and mapping (SLAM) formulate the inference problem as a high-dimensional but sparse nonconvex M-estimation, and then apply general first- or second-order smooth optimization methods to recover a local minimizer of the objective function. The performance…

Cited by 72Source ↗Scholar ↗
2015

Lagrangian duality in 3D SLAM: Verification techniques and optimal solutions

Luca Carlone, David M. Rosen, Giuseppe Calafiore, John J. Leonard, Frank Dellaert
IROS 2015poster

State-of-the-art techniques for simultaneous localization and mapping (SLAM) employ iterative nonlinear optimization methods to compute an estimate for robot poses. While these techniques often work well in practice, they do not provide guarantees on the quality of the estimate. This paper shows tha…

Cited by 123Source ↗Scholar ↗