67 citations · 154 across the 10 of their papers we have counts for
10 papers
Image-to-Point Cloud Registration Made Easy with Rectified Flow-based LiDAR Upsampling
Reon Tabata, Kenji Koide, Shuji Oishi +4
Image-to-Point Cloud Registration (I2P) is essential for integrating camera and LiDAR in perception and autonomous systems, yet the modality gap between images and point clouds mak…
Towards Compact Autonomous Driving Perception with Balanced Learning and Multi-sensor Fusion
Oskar Natan, Jun Miura
We present a novel compact deep multi-task learning model to handle various autonomous driving perception tasks in one forward pass. The model performs multiple views of semantic s…
DeepIPCv2: LiDAR-powered Robust Environmental Perception and Navigational Control for Autonomous Vehicle
Oskar Natan, Jun Miura
We propose DeepIPCv2, an end-to-end autonomous driving framework that integrates LiDAR-based environmental perception with command-specific control learning. Unlike prior camera-re…
Online Refinement of a Scene Recognition Model for Mobile Robots by Observing Human's Interaction with Environments
Shigemichi Matsuzaki, Hiroaki Masuzawa, Jun Miura
This paper describes a method of online refinement of a scene recognition model for robot navigation considering traversable plants, flexible plant parts which a robot can push asi…
DeepIPC: Deeply Integrated Perception and Control for an Autonomous Vehicle in Real Environments
Oskar Natan, Jun Miura
In this work, we introduce DeepIPC, a novel end-to-end model tailored for autonomous driving, which seamlessly integrates perception and control tasks. Unlike traditional models th…
End-to-end Autonomous Driving with Semantic Depth Cloud Mapping and Multi-agent
Oskar Natan, Jun Miura
Focusing on the task of point-to-point navigation for an autonomous driving vehicle, we propose a novel deep learning model trained with end-to-end and multi-task learning manners…
Image-based scene recognition for robot navigation considering traversable plants and its manual annotation-free training
Shigemichi Matsuzaki, Hiroaki Masuzawa, Jun Miura
This paper describes a method of estimating the traversability of plant parts covering a path and navigating through them for mobile robots operating in plant-rich environments. Co…
Multi-task Learning with Attention for End-to-end Autonomous Driving
Keishi Ishihara, Anssi Kanervisto, Jun Miura +1
Autonomous driving systems need to handle complex scenarios such as lane following, avoiding collisions, taking turns, and responding to traffic signals. In recent years, approache…
Multi-source Pseudo-label Learning of Semantic Segmentation for the Scene Recognition of Agricultural Mobile Robots
Shigemichi Matsuzaki, Jun Miura, Hiroaki Masuzawa
This paper describes a novel method of training a semantic segmentation model for scene recognition of agricultural mobile robots exploiting publicly available datasets of outdoor…
Early Estimation of User's Intention of Tele-Operation Using Object Affordance and Hand Motion in a Dual First-Person Vision
Motoki Kojima, Jun Miura
This paper describes a method of estimating the intention of a user's motion in a robot tele-operation scenario. One of the issues in tele-operation is latency, which occurs due to…