Author name cluster

Lionel Heng

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.

17 papers

1 author row

IROS Conference 2021 Conference Paper

Continuous-time Radar-inertial Odometry for Automotive Radars

Yin Zhi Ng
Benjamin Choi
Robby T. Tan
Lionel Heng

We present an approach for radar-inertial odometry which uses a continuous-time framework to fuse measurements from multiple automotive radars and an inertial measurement unit (IMU). Adverse weather conditions do not have a significant impact on the operating performance of radar sensors unlike that of camera and LiDAR sensors. Radar’s robustness in such conditions and the increasing prevalence of radars on passenger vehicles motivate us to look at the use of radar for ego-motion estimation. A continuous-time trajectory representation is applied not only as a framework to enable heterogeneous and asynchronous multi-sensor fusion, but also, to facilitate efficient optimization by being able to compute poses and their derivatives in closed-form and at any given time along the trajectory. We compare our continuous-time estimates to those from a discrete-time radar-inertial odometry approach and show that our continuous-time method outperforms the discrete-time method. To the best of our knowledge, this is the first time a continuous-time framework has been applied to radar-inertial odometry.

IROS Conference 2020 Conference Paper

Automatic Targetless Extrinsic Calibration of Multiple 3D LiDARs and Radars

Lionel Heng

Many self-driving vehicles use a multi-sensor system comprising multiple 3D LiDAR and radar sensors for robust all-round perception. Precise calibration of this multi-sensor system is a critical prerequisite for accurate perception data which facilitates safe operation of self-driving vehicles in highly dynamic urban environments. This paper proposes the first-known automatic targetless method for extrinsic calibration of multiple 3D LiDAR and radar sensors, and which only requires the vehicle to be driven over a short distance. The proposed method first estimates the 6-DoF pose of each LiDAR sensor with respect to the vehicle reference frame by minimizing point-to-plane distances between scans from different LiDAR sensors. In turn, a 3D map of the environment is built using data from all calibrated LiDAR sensors on the vehicle. We find the 6-DoF pose of each radar sensor with respect to the vehicle reference frame by minimizing (1) point-to-plane distances between radar scans and the 3D map, and (2) radial velocity errors. Our proposed calibration method does not require overlapping fields of view between LiDAR and radar sensors. Real-world experiments demonstrate the accuracy and repeatability of the proposed calibration method.

ICRA Conference 2019 Conference Paper

Project AutoVision: Localization and 3D Scene Perception for an Autonomous Vehicle with a Multi-Camera System

Lionel Heng
Benjamin Choi
Zhaopeng Cui
Marcel Geppert
Sixing Hu
Benson Kuan
Peidong Liu 0001
Rang M. H. Nguyen

Project AutoVision aims to develop localization and 3D scene perception capabilities for a self-driving vehicle. Such capabilities will enable autonomous navigation in urban and rural environments, in day and night, and with cameras as the only exteroceptive sensors. The sensor suite employs many cameras for both 360-degree coverage and accurate multi-view stereo; the use of low-cost cameras keeps the cost of this sensor suite to a minimum. In addition, the project seeks to extend the operating envelope to include GNSS-less conditions which are typical for environments with tall buildings, foliage, and tunnels. Emphasis is placed on leveraging multi-view geometry and deep learning to enable the vehicle to localize and perceive in 3D space. This paper presents an overview of the project, and describes the sensor suite and current progress in the areas of calibration, localization, and perception.

ICRA Conference 2019 Conference Paper

Real-Time Dense Mapping for Self-Driving Vehicles using Fisheye Cameras

Zhaopeng Cui
Lionel Heng
Ye Chuan Yeo
Andreas Geiger 0001
Marc Pollefeys
Torsten Sattler

We present a real-time dense geometric mapping algorithm for large-scale environments. Unlike existing methods which use pinhole cameras, our implementation is based on fisheye cameras whose large field of view benefits various computer vision applications for self-driving vehicles such as visual-inertial odometry, visual localization, and object detection. Our algorithm runs on in-vehicle PCs at approximately 15 Hz, enabling vision-only 3D scene perception for self-driving vehicles. For each synchronized set of images captured by multiple cameras, we first compute a depth map for a reference camera using plane-sweeping stereo. To maintain both accuracy and efficiency, while accounting for the fact that fisheye images have a lower angular resolution, we recover the depths using multiple image resolutions. We adopt the fast object detection framework, YOLOv3, to remove potentially dynamic objects. At the end of the pipeline, we fuse the fisheye depth images into the truncated signed distance function (TSDF) volume to obtain a 3D map. We evaluate our method on large-scale urban datasets, and results show that our method works well in complex dynamic environments.

IROS Conference 2018 Conference Paper

Towards Robust Visual Odometry with a Multi-Camera System

Peidong Liu 0001
Marcel Geppert
Lionel Heng
Torsten Sattler
Andreas Geiger 0001
Marc Pollefeys

We present a visual odometry (VO) algorithm for a multi-camera system and robust operation in challenging environments. Our algorithm consists of a pose tracker and a local mapper. The tracker estimates the current pose by minimizing photometric errors between the most recent keyframe and the current frame. The mapper initializes the depths of all sampled feature points using plane-sweeping stereo. To reduce pose drift, a sliding window optimizer is used to refine poses and structure jointly. Our formulation is flexible enough to support an arbitrary number of stereo cameras. We evaluate our algorithm thoroughly on five datasets. The datasets were captured in different conditions: daytime, night-time with near-infrared (NIR) illumination and nighttime without NIR illumination. Experimental results show that a multi-camera setup makes the VO more robust to challenging environments, especially night-time conditions, in which a single stereo configuration fails easily due to the lack of features.

IROS Conference 2017 Conference Paper

Direct visual odometry for a fisheye-stereo camera

Peidong Liu 0001
Lionel Heng
Torsten Sattler
Andreas Geiger 0001
Marc Pollefeys

We present a direct visual odometry algorithm for a fisheye-stereo camera. Our algorithm performs simultaneous camera motion estimation and semi-dense reconstruction. The pipeline consists of two threads: a tracking thread and a mapping thread. In the tracking thread, we estimate the camera pose via semi-dense direct image alignment. To have a wider field of view (FoV) which is important for robotic perception, we use fisheye images directly without converting them to conventional pinhole images which come with a limited FoV. To address the epipolar curve problem, plane-sweeping stereo is used for stereo matching and depth initialization. Multiple depth hypotheses are tracked for selected pixels to better capture the uncertainty characteristics of stereo matching. Temporal motion stereo is then used to refine the depth and remove false positive depth hypotheses. Our implementation runs at an average of 20 Hz on a low-end PC. We run experiments in outdoor environments to validate our algorithm, and discuss the experimental results. We experimentally show that we are able to estimate 6D poses with low drift, and at the same time, do semi-dense 3D reconstruction with high accuracy. To the best of our knowledge, there is no other existing semi-dense direct visual odometry algorithm for a fisheye-stereo camera.

IROS Conference 2016 Conference Paper

Semi-direct visual odometry for a fisheye-stereo camera

Lionel Heng
Benjamin Choi

We present a semi-direct visual odometry algorithm for a fisheye-stereo camera. In a tracking thread, we simultaneously track oriented patches and estimate the camera pose. In a mapping thread, we estimate the coordinates and surface normal for each new patch to be tracked. Estimation of the surface normals allows us to track patches over a wide variety of viewpoints. In our algorithm, we do not make use of descriptors and robust descriptor matching to find patch correspondences. Instead, we use photoconsistency-based techniques to find patch correspondences. For tracking, we use sparse model-based image alignment to find the relative motion estimate, and feature alignment to find 2D-3D patch correspondences. For mapping, we use plane-sweeping stereo to find matching patches between stereo images. We also implement a state estimator based on the Extended Kalman Filter (EKF) to fuse inertial measurements and relative pose estimates from our visual odometry implementation. We run experiments in two different outdoor environments to validate our algorithm, and discuss the experimental results. Our implementation runs at an average of 42 Hz on a commodity Intel CPU. To the best of our knowledge, there is no other existing semi-direct visual odometry algorithm for a fisheye-stereo camera.

ICRA Conference 2015 Conference Paper

Efficient visual exploration and coverage with a micro aerial vehicle in unknown environments

Lionel Heng
Alkis Gotovos
Andreas Krause 0001
Marc Pollefeys

In this paper, we propose a novel and computationally efficient algorithm for simultaneous exploration and coverage with a vision-guided micro aerial vehicle (MAV) in unknown environments. This algorithm continually plans a path that allows the MAV to fulfil two objectives at the same time while avoiding obstacles: observe as much unexplored space as possible, and observe as much of the surface of the environment as possible given viewing angle and distance constraints. The former and latter objectives are known as the exploration and coverage problems respectively. Our algorithm is particularly useful for automated 3D reconstruction at the street level and in indoor environments where obstacles are omnipresent. By solving the exploration problem, we maximize the size of the reconstructed model. By solving the coverage problem, we maximize the completeness of the model. Our algorithm leverages the state lattice concept such that the planned path adheres to specified motion constraints. Furthermore, our algorithm is computationally efficient and able to run on-board the MAV in real-time. We assume that the MAV is equipped with a forward-looking depth-sensing camera in the form of either a stereo camera or RGB-D camera. We use simulation experiments to validate our algorithm. In addition, we show that our algorithm achieves a significantly higher level of coverage as compared to an exploration-only approach while still allowing the MAV to fully explore the environment.

IROS Conference 2015 Conference Paper

Real-time 3D navigation for autonomous vision-guided MAVs

Shengdong Xu
Dominik Honegger
Marc Pollefeys
Lionel Heng

Autonomous navigation of micro aerial vehicles (MAVs) in a-priori unknown environments is one of the most challenging problems in robotics. First, a MAV has to incrementally build a 3D geometric map from raw sensor data. Then, based on the mapping information, the path planner has to search for a cost-optimal trajectory to the goal in real-time. It is common practice to discretize the search space into a state lattice; by doing so, we reduce the path planning problem with differential constraints to a graph search problem that is easier to solve. However, a regular 3D state lattice requires a large amount of memory while graph search in a regular 3D state lattice incorporating numerous states is computationally intensive. In this paper, we introduce a novel path planning algorithm which extends the concept of a regular state lattice to an octree-based state lattice, and searches for an optimal trajectory in the octree-partitioned search space. Our octree-based state lattice representation discretizes large swathes of free space into few symbolic octants, and thus, encodes a significantly fewer number of states. As a result, memory consumption is kept to a minimum, and at the same time, graph search is made more efficient. Simulation experiments demonstrate the efficiency of path planning with an octree-based state lattice, and further field trials prove the viability of this path planning algorithm.

ICRA Conference 2014 Conference Paper

Infrastructure-based calibration of a multi-camera rig

Lionel Heng
Mathias Bürki
Gim Hee Lee
Paul Timothy Furgale
Roland Siegwart
Marc Pollefeys

The online recalibration of multi-sensor systems is a fundamental problem that must be solved before complex automated systems are deployed in situations such as automated driving. In such situations, accurate knowledge of calibration parameters is critical for the safe operation of automated systems. However, most existing calibration methods for multisensor systems are computationally expensive, use installations of known fiducial patterns, and require expert supervision. We propose an alternative approach called infrastructure-based calibration that is efficient, requires no modification of the infrastructure, and is completely unsupervised. In a survey phase, a computationally expensive simultaneous localization and mapping (SLAM) method is used to build a highly accurate map of a calibration area. Once the map is built, many other vehicles are able to use it for calibration as if it were a known fiducial pattern. We demonstrate the effectiveness of this method to calibrate the extrinsic parameters of a multi-camera system. The method does not assume that the cameras have an overlapping field of view and it does not require an initial guess. As the camera rig moves through the previously mapped area, we match features between each set of synchronized camera images and the map. Subsequently, we find the camera poses and inlier 2D-3D correspondences. From the camera poses, we obtain an initial estimate of the camera extrinsics and rig poses, and optimize these extrinsics and rig poses via non-linear refinement. The calibration code is publicly available as a standalone C++ package.

IROS Conference 2013 Conference Paper

A 4-point algorithm for relative pose estimation of a calibrated camera with a known relative rotation angle

Bo Li 0018
Lionel Heng
Gim Hee Lee
Marc Pollefeys

We propose an algorithm to estimate the relative camera pose using four feature correspondences and one relative rotation angle measurement. The algorithm can be used for relative pose estimation of a rigid body equipped with a camera and a relative rotation angle sensor which can be either an odometer, an IMU or a GPS/INS system. This algorithm exploits the fact that the relative rotation angles of both the camera and relative rotation angle sensor are the same as the camera and sensor are rigidly mounted to a rigid body. Therefore, knowledge of the extrinsic calibration between the camera and sensor is not required. We carry out a quantitative comparison of our algorithm with the well-known 5-point and 1-point algorithms, and show that our algorithm exhibits the highest level of accuracy.

IROS Conference 2013 Conference Paper

A multiple-camera system calibration toolbox using a feature descriptor-based calibration pattern

Bo Li 0018
Lionel Heng
Kevin Köser
Marc Pollefeys

This paper presents a novel feature descriptor-based calibration pattern and a Matlab toolbox which uses the specially designed pattern to easily calibrate both the intrin-sics and extrinsics of a multiple-camera system. In contrast to existing calibration patterns, in particular, the ubiquitous chessboard, the proposed pattern contains many more features of varying scales; such features can be easily and automatically detected. The proposed toolbox supports the calibration of a camera system which can comprise either normal pinhole cameras or catadioptric cameras. The calibration only requires that neighboring cameras observe parts of the calibration pattern at the same time; the observed parts may not overlap at all. No overlapping fields of view are assumed for the camera system. We show that the toolbox can easily be used to automatically calibrate camera systems.

IROS Conference 2013 Conference Paper

CamOdoCal: Automatic intrinsic and extrinsic calibration of a rig with multiple generic cameras and odometry

Lionel Heng
Bo Li 0018
Marc Pollefeys

Multiple cameras are increasingly prevalent on robotic and human-driven vehicles. These cameras come in a variety of wide-angle, fish-eye, and catadioptric models. Furthermore, wheel odometry is generally available on the vehicles on which the cameras are mounted. For robustness, vision applications tend to use wheel odometry as a strong prior for camera pose estimation, and in these cases, an accurate extrinsic calibration is required in addition to an accurate intrinsic calibration. To date, there is no known work on automatic intrinsic calibration of generic cameras, and more importantly, automatic extrinsic calibration of a rig with multiple generic cameras and odometry. We propose an easy-to-use automated pipeline that handles both intrinsic and extrinsic calibration; we do not assume that there are overlapping fields of view. At the begining, we run an intrinsic calibration for each generic camera. The intrinsic calibration is automatic and requires a chessboard. Subsequently, we run an extrinsic calibration which finds all camera-odometry transforms. The extrinsic calibration is unsupervised, uses natural features, and only requires the vehicle to be driven around for a short time. The intrinsic parameters are optimized in a final bundle adjustment step in the extrinsic calibration. In addition, the pipeline produces a globally-consistent sparse map of landmarks which can be used for visual localization. The pipeline is publicly available as a standalone C++ package.

IROS Conference 2012 Conference Paper

SFly: Swarm of micro flying robots

Markus W. Achtelik
Michael Achtelik
Yorick Brunet
Margarita Chli
Savvas A. Chatzichristofis
Jean-Dominique Decotignie
Klaus-Michael Doth
Friedrich Fraundorfer

The SFly project is an EU-funded project, with the goal to create a swarm of autonomous vision controlled micro aerial vehicles. The mission in mind is that a swarm of MAV's autonomously maps out an unknown environment, computes optimal surveillance positions and places the MAV's there and then locates radio beacons in this environment. The scope of the work includes contributions on multiple different levels ranging from theoretical foundations to hardware design and embedded programming. One of the contributions is the development of a new MAV, a hexacopter, equipped with enough processing power for onboard computer vision. A major contribution is the development of monocular visual SLAM that runs in real-time onboard of the MAV. The visual SLAM results are fused with IMU measurements and are used to stabilize and control the MAV. This enables autonomous flight of the MAV, without the need of a data link to a ground station. Within this scope novel analytical solutions for fusing IMU and vision measurements have been derived. In addition to the realtime local SLAM, an offline dense mapping process has been developed. For this the MAV's are equipped with a payload of a stereo camera system. The dense environment map is used to compute optimal surveillance positions for a swarm of MAV's. For this an optimiziation technique based on cognitive adaptive optimization has been developed. Finally, the MAV's have been equipped with radio transceivers and a method has been developed to locate radio beacons in the observed environment.

IROS Conference 2012 Conference Paper

Vision-based autonomous mapping and exploration using a quadrotor MAV

Friedrich Fraundorfer
Lionel Heng
Dominik Honegger
Gim Hee Lee
Lorenz Meier
Petri Tanskanen
Marc Pollefeys

In this paper, we describe our autonomous vision-based quadrotor MAV system which maps and explores unknown environments. All algorithms necessary for autonomous mapping and exploration run on-board the MAV. Using a front-looking stereo camera as the main exteroceptive sensor, our quadrotor achieves these capabilities with both the Vector Field Histogram+ (VFH+) algorithm for local navigation, and the frontier-based exploration algorithm. In addition, we implement the Bug algorithm for autonomous wall-following which could optionally be selected as the substitute exploration algorithm in sparse environments where the frontier-based exploration under-performs. We incrementally build a 3D global occupancy map on-board the MAV. The map is used by the VFH+ and frontier-based exploration in dense environments, and the Bug algorithm for wall-following in sparse environments. During the exploration phase, images from the front-looking camera are transmitted over Wi-Fi to the ground station. These images are input to a large-scale visual SLAM process running off-board on the ground station. SLAM is carried out with pose-graph optimization and loop closure detection using a vocabulary tree. We improve the robustness of the pose estimation by fusing optical flow and visual odometry. Optical flow data is provided by a customized downward-looking camera integrated with a microcontroller while visual odometry measurements are derived from the front-looking stereo camera. We verify our approaches with experimental results.

ICRA Conference 2011 Conference Paper

Autonomous obstacle avoidance and maneuvering on a vision-guided MAV using on-board processing

Lionel Heng
Lorenz Meier
Petri Tanskanen
Friedrich Fraundorfer
Marc Pollefeys

We present a novel stereo-based obstacle avoidance system on a vision-guided micro air vehicle (MAV) that is capable of fully autonomous maneuvers in unknown and dynamic environments. All algorithms run exclusively on the vehicle's on-board computer, and at high frequencies that allow the MAV to react quickly to obstacles appearing in its flight trajectory. Our MAV platform is a quadrotor aircraft equipped with an inertial measurement unit and two stereo rigs. An obstacle mapping algorithm processes stereo images, producing a 3D map representation of the environment; at the same time, a dynamic anytime path planner plans a collision-free path to a goal point.

IROS Conference 2011 Conference Paper

Real-time photo-realistic 3D mapping for micro aerial vehicles

Lionel Heng
Gim Hee Lee
Friedrich Fraundorfer
Marc Pollefeys

In this paper, we proposed a method to recognize complex human daily activities including body activities and hand gestures simultaneously in an indoor environment. Three wearable motion sensors are attached to the right thigh, the waist, and the right hand of a person, while an optical motion capture system is used to obtain his/her location information. A three-level dynamic Bayesian network is implemented to model the intra-temporal and inter-temporal constraints among the location, body activity and hand gesture. The body activity and hand gesture are estimated using a Bayesian filter and the short-time Viterbi algorithm, which reduces the storage memory and the computational complexity. We conducted experiments in a mock apartment environment and the obtained results showed the effectiveness and accuracy of our algorithms.