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Muhamad Risqi U. Saputra

8 accepted papers

2026

Coarse-to-Fine Domain Incremental Learning with Attentive Distillation for Mining Footprint Segmentation in Multispectral Imagery

Alif Tri Handoyo, Vincent C.S. Lee, Rizka Widyarini Purwanto, Alex M. Lechner, Deanna Kemp, Muhamad Risqi U. Saputra
IJCAI 2026

Automatically mapping and segmenting global mining footprints using remote sensing and deep learning is critical for monitoring the socio-environmental risks and impacts of mining, yet its progress is hindered by the scarcity of fine-grained annotated data. Although large-scale datasets with coarse

Cited by 0Scholar ↗
2023

RADA: Robust Adversarial Data Augmentation for Camera Localization in Challenging Conditions

Jialu Wang, Muhamad Risqi U. Saputra, Chris Xiaoxuan Lu, Niki Trigoni, Andrew Markham
IROS 2023poster

Camera localization is a fundamental problem for many applications in computer vision, robotics, and autonomy. Despite recent deep learning-based approaches, the lack of robustness in challenging conditions persists due to changes in appearance caused by texture-less planes, repeating structures, re…

Cited by 3Source ↗code ↗Scholar ↗
2022

OdomBeyondVision: An Indoor Multi-modal Multi-platform Odometry Dataset Beyond the Visible Spectrum

Peize Li, Kaiwen Cai, Muhamad Risqi U. Saputra, Zhuangzhuang Dai, Chris Xiaoxuan Lu
IROS 2022poster

This paper presents a multimodal indoor odometry dataset, OdomBeyondVision, featuring multiple sensors across the different spectrum and collected with different mobile platforms. Not only does OdomBeyondVision contain the traditional navigation sensors, sensors such as IMUs, mechanical LiDAR, RGBD…

Cited by 15Source ↗code ↗Scholar ↗
2021

VMLoc: Variational Fusion For Learning-Based Multimodal Camera Localization

Kaichen Zhou, Changhao Chen, Bing Wang, Muhamad Risqi U. Saputra, Niki Trigoni, Andrew Markham
AAAI 2021technical

Recent learning-based approaches have achieved impressive results in the field of single-shot camera localization. However, how best to fuse multiple modalities (e.g., image and depth) and to deal with degraded or missing input are less well studied. In particular, we note that previous approaches t…

Cited by 26Source ↗PDF ↗code ↗Scholar ↗
2019

DeepPCO: End-to-End Point Cloud Odometry through Deep Parallel Neural Network

Wei Wang, Muhamad Risqi U. Saputra, Peijun Zhao, Pedro Gusmao, Bo Yang, Changhao Chen +2
IROS 2019poster

Odometry is of key importance for localization in the absence of a map. There is considerable work in the area of visual odometry (VO), and recent advances in deep learning have brought novel approaches to VO, which directly learn salient features from raw images. These learning-based approaches hav…

Cited by 61Source ↗Scholar ↗
2019

Distilling Knowledge From a Deep Pose Regressor Network

Muhamad Risqi U. Saputra, Pedro P. B. de Gusmao, Yasin Almalioglu, Andrew Markham, Niki Trigoni
ICCV 2019poster

This paper presents a novel method to distill knowledge from a deep pose regressor network for efficient Visual Odometry (VO). Standard distillation relies on "dark knowledge" for successful knowledge transfer. As this knowledge is not available in pose regression and the teacher prediction is not a…

Cited by 133PDF ↗Scholar ↗
2019

GANVO: Unsupervised Deep Monocular Visual Odometry and Depth Estimation with Generative Adversarial Networks

Yasin Almalioglu, Muhamad Risqi U. Saputra, Pedro P. B. de Gusmão, Andrew Markham, Niki Trigoni
ICRA 2019poster

In the last decade, supervised deep learning approaches have been extensively employed in visual odometry (VO) applications, which is not feasible in environments where labelled data is not abundant. On the other hand, unsupervised deep learning approaches for localization and mapping in unknown env…

Cited by 199Source ↗Scholar ↗
2019

Learning Monocular Visual Odometry through Geometry-Aware Curriculum Learning

Muhamad Risqi U. Saputra, Pedro P. B. de Gusmao, Sen Wang, Andrew Markham, Niki Trigoni
ICRA 2019poster

Inspired by the cognitive process of humans and animals, Curriculum Learning (CL) trains a model by gradually increasing the difficulty of the training data. In this paper, we study whether CL can be applied to complex geometry problems like estimating monocular Visual Odometry (VO). Unlike existing…

Cited by 58Source ↗Scholar ↗