CamVox: A Low-cost and Accurate Lidar-assisted Visual SLAM System

Zhu, Yuewen; Zheng, Chunran; Yuan, Chongjian; Huang, Xu; Hong, Xiaoping

doi:10.1109/icra48506.2021.9561149

Cited by 74 publications

(23 citation statements)

References 22 publications

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“…In our sensor fusion system, the Livox Avia LiDAR [ 12 , 13 ] and the FLIR polarization color cameras equipped with the Sony IMX250MYR CMOS [ 29 ] are utilized, as shown in Figure 2 a. They are mounted on a frame fabricated by CNC machining, and their positions are fixed.…”

Section: Methodsmentioning

confidence: 99%

“…Conversely, the LiDAR based on the direct time of flight (dTOF) principle has high ranging accuracy and stable measurement results. Unlike the traditional mechanical LiDAR that uses high-grade optics and a rotating assembly to create a 360° field of view (FOV), the Livox Avia LiDAR based on prism scanning is employed in our paper [ 12 , 13 ]. With the advantage of low price and excellent performance, the Avia is an ideal selection.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, in recent years, the LiDAR technology has developed speedily, which makes it widely used in various fields. The forward-looking LiDAR [ 12 , 13 ] has the potential to deliver lifesaving and cost-saving benefits. Nevertheless, the LiDAR provides limited angular resolution.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Unifying Obstacle Detection, Recognition, and Fusion Based on the Polarization Color Stereo Camera and LiDAR for the ADAS

Long

Han²,

Wang

et al. 2022

Sensors

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The perception module plays an important role in vehicles equipped with advanced driver-assistance systems (ADAS). This paper presents a multi-sensor data fusion system based on the polarization color stereo camera and the forward-looking light detection and ranging (LiDAR), which achieves the multiple target detection, recognition, and data fusion. The You Only Look Once v4 (YOLOv4) network is utilized to achieve object detection and recognition on the color images. The depth images are obtained from the rectified left and right images based on the principle of the epipolar constraints, then the obstacles are detected from the depth images using the MeanShift algorithm. The pixel-level polarization images are extracted from the raw polarization-grey images, then the water hazards are detected successfully. The PointPillars network is employed to detect the objects from the point cloud. The calibration and synchronization between the sensors are accomplished. The experiment results show that the data fusion enriches the detection results, provides high-dimensional perceptual information and extends the effective detection range. Meanwhile, the detection results are stable under diverse range and illumination conditions.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Unifying Obstacle Detection, Recognition, and Fusion Based on the Polarization Color Stereo Camera and LiDAR for the ADAS

Long

Han²,

Wang

et al. 2022

Sensors

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show abstract

“…The method associated the visual feature with depth measurement and uses it to estimate the camera motion. Recent of the development of the low cost solid-state LiDAR, there are also emerging in large numbers visual LiDAR fusion approaches using the Field of View (FoV) LiDAR, such as R 2 LIVE [49], and CamVox [50]. Compared with the traditional mechanical LiDAR, although the FoV is smaller, the solid-state LiDAR is relatively cheap and has a higher point cloud density.…”

Section: Visual Odometry Based Methodsmentioning

confidence: 99%

DV-LOAM: Direct Visual LiDAR Odometry and Mapping

et al. 2021

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Self-driving cars have experienced rapid development in the past few years, and Simultaneous Localization and Mapping (SLAM) is considered to be their basic capabilities. In this article, we propose a direct vision LiDAR fusion SLAM framework that consists of three modules. Firstly, a two-staged direct visual odometry module, which consists of a frame-to-frame tracking step, and an improved sliding window based thinning step, is proposed to estimate the accurate pose of the camera while maintaining efficiency. Secondly, every time a keyframe is generated, a dynamic objects considered LiDAR mapping module is utilized to refine the pose of the keyframe to obtain higher positioning accuracy and better robustness. Finally, a Parallel Global and Local Search Loop Closure Detection (PGLS-LCD) module that combines visual Bag of Words (BoW) and LiDAR-Iris feature is applied for place recognition to correct the accumulated drift and maintain a globally consistent map. We conducted a large number of experiments on the public dataset and our mobile robot dataset to verify the effectiveness of each module in our framework. Experimental results show that the proposed algorithm achieves more accurate pose estimation than the state-of-the-art methods.

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“…In order to further improve the robustness of the SLAM system, some researchers have integrated the LiDAR subsystem with the visual subsystem. Zhu et al introduced the LiDAR data into the Orb-Slam2 by taking advantage of the unique characteristics of the LiDAR data to realize the integration of LiDAR odometry and visual odometry [27]. On the basis of LIO-SAM, Shan et al integrated stereo camera, LiDAR, IMU, and GPS based on the image optimization method and introduced global attitude map optimization based on GPS and loop, which can eliminate cumulative drift.…”

Section: Relate Workmentioning

confidence: 99%

Marked-LIEO: Visual Marker-Aided LiDAR/IMU/Encoder Integrated Odometry

Chen

Zhao

Zhu

et al. 2022

Sensors

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In this paper, we propose a visual marker-aided LiDAR/IMU/encoder integrated odometry, Marked-LIEO, to achieve pose estimation of mobile robots in an indoor long corridor environment. In the first stage, we design the pre-integration model of encoder and IMU respectively to realize the pose estimation combined with the pose estimation from the second stage providing prediction for the LiDAR odometry. In the second stage, we design low-frequency visual marker odometry, which is optimized jointly with LiDAR odometry to obtain the final pose estimation. In view of the wheel slipping and LiDAR degradation problems, we design an algorithm that can make the optimization weight of encoder odometry and LiDAR odometry adjust adaptively according to yaw angle and LiDAR degradation distance respectively. Finally, we realize the multi-sensor fusion localization through joint optimization of an encoder, IMU, LiDAR, and camera measurement information. Aiming at the problems of GNSS information loss and LiDAR degradation in indoor corridor environment, this method introduces the state prediction information of encoder and IMU and the absolute observation information of visual marker to achieve the accurate pose of indoor corridor environment, which has been verified by experiments in Gazebo simulation environment and real environment.

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CamVox: A Low-cost and Accurate Lidar-assisted Visual SLAM System

Cited by 74 publications

References 22 publications

Unifying Obstacle Detection, Recognition, and Fusion Based on the Polarization Color Stereo Camera and LiDAR for the ADAS

Unifying Obstacle Detection, Recognition, and Fusion Based on the Polarization Color Stereo Camera and LiDAR for the ADAS

DV-LOAM: Direct Visual LiDAR Odometry and Mapping

Marked-LIEO: Visual Marker-Aided LiDAR/IMU/Encoder Integrated Odometry

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