Author name cluster

Jiawei Hou

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

2 author rows

AAAI Conference 2026 Conference Paper

LOG-Nav: Efficient Layout-Aware Object-Goal Navigation with Hierarchical Planning

Jiawei Hou
Yuting Xiao
Xiangyang Xue
Taiping Zeng

We introduce LOG-Nav, an efficient layout-aware object-goal navigation approach designed for complex multi-room indoor environments. By planning hierarchically leveraging a global topologigal map with layout information and local imperative approach with detailed scene representation memory, LOG-Nav achieves both efficient and effective navigation. The process is managed by an LLM-powered agent, ensuring seamless effective planning and navigation, without the need for human interaction, complex rewards, or costly training. Our experimental results on the MP3D benchmark achieves 85% object navigation success rate (SR) and 79% success rate weighted by path length (SPL) (over 40% point improvement in SR and 60% improvement in SPL compared to exsisting methods). Furthermore, we validate the robustness of our approach through virtual agent and real-world robotic deployment, showcasing its capability in practical scenarios.

PDF Details DOI

ICRA Conference 2024 Conference Paper

FastOcc: Accelerating 3D Occupancy Prediction by Fusing the 2D Bird's-Eye View and Perspective View

Jiawei Hou
Xiaoyan Li
Wenhao Guan
Gang Zhang
Di Feng
Yuheng Du
Xiangyang Xue 0001
Jian Pu

In autonomous driving, 3D occupancy prediction outputs voxel-wise status and semantic labels for more comprehensive understandings of 3D scenes compared with traditional perception tasks, such as 3D object detection and bird’s-eye view (BEV) semantic segmentation. Recent researchers have extensively explored various aspects of this task, including view transformation techniques, ground-truth label generation, and elaborate network design, aiming to achieve superior performance. However, the inference speed, crucial for running on an autonomous vehicle, is neglected. To this end, a new method, dubbed FastOcc, is proposed. By carefully analyzing the network effect and latency from four parts, including the input image resolution, image backbone, view transformation, and occupancy prediction head, it is found that the occupancy prediction head holds considerable potential for accelerating the model while keeping its accuracy. Targeted at improving this component, the time-consuming 3D convolution network is replaced with a novel residual-like architecture, where features are mainly digested by a lightweight 2D BEV convolution network and compensated by integrating the 3D voxel features interpolated from the original image features. Experiments on the Occ3D-nuScenes benchmark demonstrate that our FastOcc achieves state-of-the-art results with a fast inference speed.

Details

ICRA Conference 2023 Conference Paper

FloorplanNet: Learning Topometric Floorplan Matching for Robot Localization

Delin Feng
Zhenpeng He
Jiawei Hou
Sören Schwertfeger
Liangjun Zhang

Given a building floorplan, humans can localize themselves by matching the observation of the environment with the floorplan using geometric, semantic, and topological clues. Inspired by this insight, this paper proposes a learning- based topometric robot localization method FloorplanNet, which implements a match between a metric robot map and the potentially inaccurate building floorplan in nonuniform scales and different shapes by semantic information. The method uses a novel Graph Neural Network to learn descriptors of nodes from topometric graphs generated from the input maps. We demonstrate that our method can match the 3D point cloud sub-map generated by the robot during the SLAM process with the 2D map. Furthermore, we apply our map-matching algorithm for real-world robot localization. We evaluate our method on several publicly available real-world datasets. Even though our network is solely trained using simulation data, our method demonstrates high robustness and effectiveness in real- world indoor environments and outperforms the existing SOTA map-matching algorithms. We further develop a simulator that automatically creates and annotates the required training data to train our neural networks. The method and simulator are released at: https://github.com/fengdelin/FloorplanNet.git

Details

IROS Conference 2022 Conference Paper

Multical: Spatiotemporal Calibration for Multiple IMUs, Cameras and LiDARs

Xiangyang Zhi
Jiawei Hou
Yiren Lu 0002
Laurent Kneip
Sören Schwertfeger

Spatiotemporal calibration of sensors, especially of those which do not share their fields of view, is becoming increasingly important in the fields of autonomous driving and robotics. This paper presents a general sensor calibration method, named Multical, that makes use of multiple planar calibration targets whose poses will be estimated alongside spatiotemporal calibration. Multical exploits continuous-time curves to represent the state of the sensor platform during data collection, and thus is a general framework to calibrate different kinds of sensors and deal with both spatial as well as temporal offsets. Multical includes algorithms to estimate the initial guesses of spatial transformations between sensors, and also the relative poses between calibration targets. Users do not need to provide any extrinsic priors. We apply the proposed calibration approach to both simulated and real-world experiments, and the results demonstrate the high fidelity of the proposed method.

Details