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Long Chen 0005

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12 papers

1 author row

IROS Conference 2025 Conference Paper

Adjacent-view Transformers for Supervised Surround-view Depth Estimation

Xianda Guo
Wenjie Yuan
Yunpeng Zhang
Tian Yang
Chenming Zhang
Zheng Zhu
Qin Zou 0001
Long Chen 0005

Depth estimation has been widely studied and serves as the fundamental step of 3D perception for robotics and autonomous driving. Though significant progress has been made in monocular depth estimation in the past decades, these attempts are mainly conducted on the KITTI benchmark with only front-view cameras, which ignores the correlations across surround-view cameras. In this paper, we propose an Adjacent-View Transformer for Supervised Surround-view Depth estimation (AVT-SSDepth), to jointly predict the depth maps across multiple surrounding cameras. Specifically, we employ a global-to-local feature extraction module that combines CNN with transformer layers for enriched representations. Further, the adjacent-view attention mechanism is proposed to enable the intra-view and inter-view feature propagation. The former is achieved by the self-attention module within each view, while the latter is realized by the adjacent attention module, which computes the attention across multi-cameras to exchange the multi-scale representations across surround-view feature maps. In addition, AVT-SSDepth has strong cross-dataset generalization. Extensive experiments show that our method achieves superior performance over existing state-of-the-art methods on both DDAD and nuScenes datasets. Code is available at https://github.com/XiandaGuo/SSDepth.

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IROS Conference 2025 Conference Paper

EANS: Reducing Energy Consumption for UAV with an Environmental Adaptive Navigation Strategy

Tian Liu
Han Liu
Boyang Li 0009
Long Chen 0005
Kai Huang 0001

Unmanned Aerial Vehicles (Uavs) are limited by the onboard energy. Refinement of the navigation strategy directly affects both the flight velocity and the trajectory based on the adjustment of key parameters in the Uavs pipeline, thus reducing energy consumption. However, existing techniques tend to adopt static and conservative strategies in dynamic scenarios, leading to inefficient energy reduction. Dynamically adjusting the navigation strategy requires overcoming the challenges including the task pipeline interdependencies, the environmental-strategy correlations, and the selecting parameters. To solve the aforementioned problems, this paper proposes a method to dynamically adjust the navigation strategy of the Uavs by analyzing its dynamic characteristics and the temporal characteristics of the autonomous navigation pipeline, thereby reducing Uavs energy consumption in response to environmental changes. We compare our method with the baseline through hardware-in-the-loop (HIL) simulation and real-world experiments, showing our method 3. 2X and 2. 6X improvements in mission time, 2. 4X and 1. 6X improvements in energy, respectively.

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ICRA Conference 2025 Conference Paper

Lightstereo: Channel Boost is All You Need for Efficient 2D Cost Aggregation

Xianda Guo
Chenming Zhang
Youmin Zhang 0008
Wenzhao Zheng
Dujun Nie
Matteo Poggi
Long Chen 0005

We present LightStereo, a cutting-edge stereomatching network crafted to accelerate the matching process. Departing from conventional methodologies that rely on aggregating computationally intensive 4D costs, LightStereo adopts the 3D cost volume as a lightweight alternative. While similar approaches have been explored previously, our breakthrough lies in enhancing performance through a dedicated focus on the channel dimension of the 3D cost volume, where the distribution of matching costs is encapsulated. Our exhaustive exploration has yielded plenty of strategies to amplify the capacity of the pivotal dimension, ensuring both precision and efficiency. We compare the proposed LightStereo with existing state-of-the-art methods across various benchmarks, which demonstrate its superior performance in speed, accuracy, and resource utilization. LightStereo achieves a competitive EPE metric in the SceneFlow datasets while demanding a minimum of only 22 GFLOPs and 17 ms of runtime, and ranks 1st on KITTI 2015 among real-time models. Our comprehensive analysis reveals the effect of 2 D cost aggregation for stereo matching, paving the way for realworld applications of efficient stereo systems. Code is available at https://github.com/XiandaGuo/OpenStereo.

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IROS Conference 2025 Conference Paper

WMNav: Integrating Vision-Language Models into World Models for Object Goal Navigation

Dujun Nie
Xianda Guo
Yiqun Duan
Ruijun Zhang
Long Chen 0005

Object Goal Navigation-requiring an agent to locate a specific object in an unseen environment-remains a core challenge in embodied AI. Although recent progress in Vision-Language Model (VLM)-based agents has demonstrated promising perception and decision-making abilities through prompting, none has yet established a fully modular world model design that reduces risky and costly interactions with the environment by predicting the future state of the world. We introduce WMNav, a novel World Model-based Navigation framework powered by Vision-Language Models (VLMs). It predicts possible outcomes of decisions and builds memories to provide feedback to the policy module. To retain the predicted state of the environment, WMNav proposes the online maintained Curiosity Value Map as part of the world model memory to provide dynamic configuration for navigation policy. By decomposing according to a human-like thinking process, WMNav effectively alleviates the impact of model hallucination by making decisions based on the feedback difference between the world model plan and observation. To further boost efficiency, we implement a two-stage action proposer strategy: broad exploration followed by precise localization. Extensive evaluation on HM3D and MP3D validates WMNav surpasses existing zero-shot benchmarks in both success rate and exploration efficiency (absolute improvement: +3. 2% SR and +3. 2% SPL on HM3D, +13. 5% SR and +1. 1% SPL on MP3D). Project page: https://b0b8k1ng.github.io/WMNav/.

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IROS Conference 2024 Conference Paper

AdvDiffuser: Generating Adversarial Safety-Critical Driving Scenarios via Guided Diffusion

Yuting Xie
Xianda Guo
Cong Wang
Kunhua Liu
Long Chen 0005

Safety-critical scenarios are infrequent in natural driving environments but hold significant importance for the training and testing of autonomous driving systems. The prevailing approach involves generating safety-critical scenarios automatically in simulation by introducing adversarial adjustments to natural environments. These adjustments are often tailored to specific tested systems, thereby disregarding their transferability across different systems. In this paper, we propose AdvDiffuser, an adversarial framework for generating safety-critical driving scenarios through guided diffusion. By incorporating a diffusion model to capture plausible collective behaviors of background vehicles and a lightweight guide model to effectively handle adversarial scenarios, AdvDiffuser facilitates transferability. Experimental results on the nuScenes dataset demonstrate that AdvDiffuser, trained on offline driving logs, can be applied to various tested systems with minimal warm-up episode data and outperform other existing methods in terms of realism, diversity, and adversarial performance.

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IROS Conference 2022 Conference Paper

ULSM: Underground Localization and Semantic Mapping with Salient Region Loop Closure under Perceptually-Degraded Environment

Junhui Wang
Bin Tian 0003
Rui Zhang
Long Chen 0005

Simultaneous Localization and Mapping (SLAM) has greatly assisted in exploring perceptually-degraded underground environments, such as human-made tunnels, mine tunnels, and caves. However, the recurring sensor failures and spurious loop closures in these scenes bring significant challenges to applying SLAM. This paper proposes an architecture for underground localization and semantic mapping (ULSM) that promotes the robustness of odometry estimation and map-building. In this architecture, a two-stage robust motion compensation method is proposed to adapt to sensor-failure situations. The proposed salient region loop closure detection contributes to avoiding spurious loop closures. Meanwhile, the 2D pose as the initial value for point cloud registration is estimated without additional input. We also design a multi-robot cooperative mapping scheme based on descriptors of the salient region. Extensive experiments are conducted on datasets collected in the Tunnel Circuit of DARPA Subterranean Challenge.

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IROS Conference 2020 Conference Paper

GOSMatch: Graph-of-Semantics Matching for Detecting Loop Closures in 3D LiDAR data

Yachen Zhu
Yanyang Ma
Long Chen 0005
Cong Liu
Maosheng Ye
Lingxi Li 0001

Detecting loop closures in 3D Light Detection and Ranging (LiDAR) data is a challenging task since point-level methods always suffer from instability. This paper presents a semantic-level approach named GOSMatch to perform reliable place recognition. Our method leverages novel descriptors, which are generated from the spatial relationship between semantics, to perform frame description and data association. We also propose a coarse-to-fine strategy to efficiently search for loop closures. Besides, GOSMatch can give an accurate 6-DOF initial pose estimation once a loop closure is confirmed. Extensive experiments have been conducted on the KITTI odometry dataset and the results show that GOSMatch can achieve robust loop closure detection performance and outperform existing methods.

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ICRA Conference 2020 Conference Paper

Imitative Reinforcement Learning Fusing Vision and Pure Pursuit for Self-driving

Mingxing Peng
Zhihao Gong
Chen Sun 0008
Long Chen 0005
Dongpu Cao

Autonomous urban driving navigation is still an open problem and has ample room for improvement in unknown complex environments and terrible weather conditions. In this paper, we propose a two-stage framework, called IPP-RL, to handle these problems. IPP means an Imitation learning method fusing visual information with the additional steering angle calculated by Pure-Pursuit (PP) method, and RL means using Reinforcement Learning for further training. In our IPP model, the visual information captured by camera can be compensated by the calculated steering angle, thus it could perform well under bad weather conditions. However, imitation learning performance is limited by the driving data severely. Thus we use a reinforcement learning method-Deep Deterministic Policy Gradient (DDPG)-in the second stage training, which shares the learned weights from pretrained IPP model. In this way, our IPP-RL can lower the dependency of imitation learning on demonstration data and solve the problem of low exploration efficiency caused by randomly initialized weights in reinforcement learning. Moreover, we design a more reasonable reward function and use the n-step return to update the critic-network in DDPG. Our experiments on CARLA driving benchmark demonstrate that our IPP-RL is robust to lousy weather conditions and shows remarkable generalization capability in unknown environments on navigation task.

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ICRA Conference 2019 Conference Paper

DSNet: Joint Learning for Scene Segmentation and Disparity Estimation

Wujing Zhan
Xinqi Ou
Yunyi Yang
Long Chen 0005

Recently, research works have attempted the joint prediction of scene semantics and optical flow estimation, which demonstrate the mutual improvement between both tasks. Besides, the depth information is also indispensable for the scene understanding, and disparity estimation is necessary for outputting dense depth maps. Such task shares a great similarity with the optical flow estimation since they can all be cast into a problem of capturing the difference at a location of two image frames. However, as far as we know, currently there are few networks for the joint learning of semantic and disparity. Moreover, since deep semantic information and disparity feature maps can learn from each other, we find it unnecessary with two independent encoding modules to separately extract semantic and disparity features. Therefore, we propose a unified multi-tasking architecture DSNet, for the simultaneous estimation of semantic and disparity information. In our model, semantic features, extracted by the encoding module ResNet from the left and right images, are used to obtain the deep disparity features via a novel matching module which performs pixel-to-pixel matching. In addition, we also use the disparity map to perform warp operation on deep features of the right image to deal with the problem of lacking of semantic labels. The effectiveness of our method is demonstrated by extensive experiments.

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IROS Conference 2019 Conference Paper

Monocular Outdoor Semantic Mapping with a Multi-task Network

Yucai Bai
Lei Fan 0005
Ziyu Pan
Long Chen 0005

In many robotic applications, especially for the autonomous driving, understanding the semantic information and the geometric structure of surroundings are both essential. Semantic 3D maps, as a carrier of the environmental knowledge, are then intensively studied for their abilities and applications. However, it is still challenging to produce a dense outdoor semantic map from a monocular image stream. Motivated by this target, in this paper, we propose a method for large-scale 3D reconstruction from consecutive monocular images. First, with the correlation of underlying information between depth and semantic prediction, a novel multi-task Convolutional Neural Network (CNN) is designed for joint prediction. Given a single image, the network learns low-level information with a shared encoder and separately predicts with decoders containing additional Atrous Spatial Pyramid Pooling (ASPP) layers and the residual connection which merits disparities and semantic mutually. To overcome the inconsistency of monocular depth prediction for reconstruction, post-processing steps with the superpixelization and the effective 3D representation approach are obtained to give the final semantic map. Experiments are compared with other methods on both semantic labeling and depth prediction. We also qualitatively demonstrate the map reconstructed from large-scale, difficult monocular image sequences to prove the effectiveness and superiority.

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ICRA Conference 2019 Conference Paper

Real-Time Vehicle Detection from Short-range Aerial Image with Compressed MobileNet

Yuhang He
Ziyu Pan
Lingxi Li 0001
Yunxiao Shan
Dongpu Cao
Long Chen 0005

Vehicle detection from short-range aerial image faces challenges including vehicle blocking, irrelevant object interference, motion blurring, color variation etc. , leading to the difficulty to achieve high detection accuracy and real-time detection speed. In this paper, benefiting from the recent development in MobileNet family network engineering, we propose a compressed MobileNet which is not only internally resistant to the above listed challenges but also gains the best detection accuracy/speed tradeoff when comparing with the original MobileNet. In a nutshell, we reduce the bottleneck architecture number during the feature map downsampling stage but add more bottlenecks during the feature map plateau stage, neither extra FLOPs nor parameters are thus involved but reduced inference time and better accuracy are expected. We conduct experiment on our collected 5-k short-range aerial images, containing six vehicle categories: truck, car, bus, bicycle, motorcycle, crowded bicycles and crowded motorcycles. Our proposed compressed MobileNet achieves 110 FPS (GPU), 31 FPS (CPU) and 15 FPS (mobile phone), 1. 2 times faster and 2% more accurate (mAP) than the original MobileNet.

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ICRA Conference 2017 Conference Paper

RGB-T SLAM: A flexible SLAM framework by combining appearance and thermal information

Long Chen 0005
Libo Sun 0002
Teng Yang
Lei Fan 0005
Kai Huang 0001
Zhe Xuanyuan

Visual SLAM in low illumination scenes remains a considerably challenging task since the available amount of appearance information frequently stays insufficient. To tackle with this problem, we propose a novel SLAM framework by using both appearance information and thermal information, which possesses illumination-free recognizable contents, in a flexible manner. The key idea is to continuously update a RGB-T map, which contains both RGB and thermal map points to implement location and mapping. More specifically, in our SLAM system, we detect features in both RGB and thermal images and combine them together to update the RGB-T map and implement simultaneous location and mapping. Both quantitative and qualitative results demonstrate the effectiveness of our framework, especially under low illumination environments.

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