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Mengwen He

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.

5 papers

1 author row

IROS Conference 2021 Conference Paper

Extended VINS-Mono: A Systematic Approach for Absolute and Relative Vehicle Localization in Large-Scale Outdoor Environments

Mengwen He
Ragunathan Raj Rajkumar

We present a systematic approach called Extended VINS-Mono to utilize VINS-Mono, a state-of-the-art monocular visual-inertial relative localization method, targeting practical vehicle localization in large-scale outdoor road environments. Our proposed fusion approach associates multiple independent localization methods and provides multiple (projected) state estimates in a desired coordinate system to satisfy different accuracy, rate and latency requirements. We extend VINS-Mono with absolute localization methods like GNSS and relative localization methods like Kalman-filter-based INS to provide global state estimation for navigation/routing and local state estimation for planning/control. Additionally, Extended VINS-Mono addresses two significant drawbacks in VINS-Mono for use in large-scale outdoor road environments. First, motion on an almost planar road surface will make scale unobservable in VINS-Mono. Secondly, moving objects in dynamic scenarios will degrade accuracy. We handle the scale estimation problem of VINS-Mono by extending its (re-)initialization process with speed readings and introducing a speed factor for use with graph optimization. A dynamic feature-point filter method with masks from DNN-based object detection handles dynamic environments and re-collects feature points on stationary objects like parked cars. Better global accuracy is obtained with Extended VINS-Mono, compared to VINS-Mono, in a 25 km-trip journey through highways, tunnels, urban areas and suburban areas in Pittsburgh. Thus, Extended VINS-Mono can be used for reliable and accurate absolute localization in dynamic road environments. We also evaluate the accuracy, localization rate and latency of multiple (projected) state estimates in the global coordinate system from multiple localization methods. Our fusion method is therefore able to satisfy different localization requirements of various tasks on an intelligent vehicle.

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IROS Conference 2016 Conference Paper

Precise and efficient model-based vehicle tracking method using Rao-Blackwellized and scaling series particle filters

Mengwen He
Eijiro Takeuchi
Yoshiki Ninomiya
Shinpei Kato

A precise and efficient tracking technique is essential for an intelligent vehicle to interact with the surrounding vehicles on the road. In this paper, we propose a model-based vehicle tracking method using the Rao-Blackwellized particle filter (RBPF) and scaling series particle filter (SSPF).

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ICRA Conference 2014 Conference Paper

Calibration method for multiple 2D LIDARs system

Mengwen He
Huijing Zhao
Jinshi Cui
Hongbin Zha

Many robotic and mobile mapping systems have been developed using multiple 2D LIDARs (briefly multi-LIDAR system) to sense environment. In such systems, extrinsic calibration of all LIDARs is essential for making collaborative use of the data from different sensors. This research aims at developing a calibration method for multi-LIDAR systems at the general scene, such as an outdoor place or an underground parking-lot, without modification to environment by putting calibration targets. In this paper, the calibration method is proposed by aligning the 3D data of different LIDARs. They are concerned at two-levels: 1) reference calibration, i. e. finding the transformation from a reference LIDAR to the platform frame; 2) multi-LIDAR calibration, i. e. finding the LIDARs' relative geometries by referring to the reference one. The method is examined in calibrating the multiple 2D LIDARs on an intelligent vehicle platform POSS-V, where the data collected through a driving in an underground parking-lot are registered to find sensors' geometry. Calibration accuracy is examined by comparing with a CAD model of the scene, which was measured by using a total station.

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IROS Conference 2013 Conference Paper

Pairwise LIDAR calibration using multi-type 3D geometric features in natural scene

Mengwen He
Huijing Zhao
Franck Davoine
Jinshi Cui
Hongbin Zha

It has become a well-known technology that 3D measurement of a large environment could be achieved by using a number of 2D LIDARs on a mobile platform. In such a system, calibration is essential for making collaborative use of different LIDAR data, while existing methods usually require modifications to the environments, such as putting calibration targets, or rely on special facilities, which is labor intensive and put many restrictions to potential applications. This research aims at developing a calibration method for multiple 2D LIDAR sensing systems, which could be conducted in a general outdoor environment using the features of a nature scene. Special focus is cast on solving the noisy sensing in a complex environment and the occlusions caused by largely different sensor viewpoints. A multi-type geometric feature based calibration algorithm is proposed, which extracts the features such as points, lines, planes and quadrics from the 3D points of each LIDAR sensing. Transformation parameters from each sensor to the frame on a moving platform is estimated by matching the multi-type features. Experiments are conducted using the data sets of an intelligent vehicle platform (POSS-V) through a driving in the campus of Peking University. Results of calibrating two LIDAR sensors with largely different viewpoints are presented, and the accuracy and robustness concerning noisy feature extractions are examined intensively.

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ICRA Conference 2012 Conference Paper

Computing object-based saliency in urban scenes using laser sensing

Yipu Zhao
Mengwen He
Huijing Zhao
Franck Davoine
Hongbin Zha

It becomes a well-known technology that a low-level map of complex environment containing 3D laser points can be generated using a robot with laser scanners. Given a cloud of 3D laser points of an urban scene, this paper proposes a method for locating the objects of interest, e. g. traffic signs or road lamps, by computing object-based saliency. Our major contributions are: 1) a method for extracting simple geometric features from laser data is developed, where both range images and 3D laser points are analyzed; 2) an object is modeled as a graph used to describe the composition of geometric features; 3) a graph matching based method is developed to locate the objects of interest on laser data. Experimental results on real laser data depicting urban scenes are presented; efficiency as well as limitations of the method are discussed.

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