Author name cluster

Phu Pham

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

IROS Conference 2025 Conference Paper

Go-SLAM: Grounded Object Segmentation and Localization with Gaussian Splatting SLAM

Phu Pham
Dipam Patel
Damon Conover
Aniket Bera

We introduce Go-Slam, a novel framework that combines 3D Gaussian Splatting SLAM with grounded object segmentation and open-vocabulary querying to enable object-aware 3D scene reconstruction. Go-Slam incrementally builds high-fidelity 3D maps from RGB-D inputs while embedding semantic information by assigning unique object identifiers to Gaussian primitives. This integration allows the system to support flexible, natural language queries and accurately localize objects in complex, static environments. To achieve robust semantic mapping, Go-Slam leverages object detection and segmentation models, enabling consistent object identification across frames without relying on predefined categories. We evaluate Go-Slam across diverse indoor scenes, demonstrating improvements over existing baselines in both reconstruction quality and object localization accuracy. Our results show that Go-Slam effectively bridges the gap between geometric mapping and semantic understanding, supporting real-time scene interaction and object retrieval in open-world environments.

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IROS Conference 2024 Conference Paper

Optimizing Crowd-Aware Multi-Agent Path Finding through Local Communication with Graph Neural Networks

Phu Pham
Aniket Bera

Multi-Agent Path Finding (MAPF) in crowded environments presents a challenging problem in motion planning, aiming to find collision-free paths for all agents in the system. MAPF finds a wide range of applications in various domains, including aerial swarms, autonomous warehouse robotics, and self-driving vehicles. Current approaches to MAPF generally fall into two main categories: centralized and decentralized planning. Centralized planning suffers from the curse of dimensionality when the number of agents or states increases and thus does not scale well in large and complex environments. On the other hand, decentralized planning enables agents to engage in real-time path planning within a partially observable environment, demonstrating implicit coordination. However, they suffer from slow convergence and performance degradation in dense environments. In this paper, we introduce CRAMP, a novel crowd-aware decentralized reinforcement learning approach to address this problem by enabling efficient local communication among agents via Graph Neural Networks (GNNs), facilitating situational awareness and decision-making capabilities in congested environments. We test CRAMP on simulated environments and demonstrate that our method outperforms the state-of-the-art decentralized methods for MAPF on various metrics. CRAMP improves the solution quality up to 59% measured in makespan and collision count, and up to 35% improvement in success rate in comparison to previous methods.

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IROS Conference 2023 Conference Paper

DroNeRF: Real-Time Multi-Agent Drone Pose Optimization for Computing Neural Radiance Fields

Dipam Patel
Phu Pham
Aniket Bera

We present a novel optimization algorithm called DroNeRF for the autonomous positioning of monocular camera drones around an object for real-time 3D reconstruction using only a few images. Neural Radiance Fields, or NeRF, is a novel view synthesis technique used to generate new views of an object or scene from a set of input images. Using drones in conjunction with NeRF provides a unique and dynamic way to generate novel views of a scene, especially with limited scene capabilities of restricted movements. Our approach focuses on calculating optimized pose for individual drones while solely depending on the object geometry without using any external localization system. The unique camera positioning during the data capturing phase significantly impacts the quality of the 3D model. To evaluate the quality of our generated novel views, we compute different perceptual metrics like the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). Our work demonstrates the benefit of using an optimal placement of various drones with limited mobility to generate perceptually better results.

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IROS Conference 2023 Conference Paper

RAIST: Learning Risk Aware Traffic Interactions via Spatio-Temporal Graph Convolutional Networks

Videsh Suman
Phu Pham
Aniket Bera

A key aspect of driving a road vehicle is to interact with other road users, assess their intentions and make riskaware tactical decisions. An intuitive approach to enabling an intelligent automated driving system would be incorporating some aspects of human driving behavior. To this end, we propose a novel driving framework for egocentric views based on spatio-temporal traffic graphs. The traffic graphs model not only the spatial interactions amongst the road users but also their individual intentions through temporally associated message passing. We leverage a spatio-temporal graph convolutional network (ST-GCN) to train the graph edges. These edges are formulated using parameterized functions of 3D positions and scene-aware appearance features of road agents. Along with tactical behavior prediction, it is crucial to evaluate the risk-assessing ability of the proposed framework. We claim that our framework learns risk-aware representations by improving on the task of risk object identification, especially in identifying objects with vulnerable interactions like pedestrians and cyclists.

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