Elastic LiDAR Fusion: Dense Map-Centric Continuous-Time SLAM

Abstract

The concept of continuous-time trajectory representation has brought increased accuracy and efficiency to multi-modal sensor fusion in modern SLAM. However, regardless of these advantages, its offline property caused by the requirement of global batch optimization is critically hindering its relevance for real-time and life-long applications. In this paper, we present a dense map-centric SLAM method based on a continuous-time trajectory to cope with this problem. The proposed system locally functions in a similar fashion to conventional Continuous-Time SLAM (CT-SLAM). However, it removes the need for global trajectory optimization by introducing map deformation. The computational complexity of the proposed approach for loop closure does not depend on the operation time, but only on the size of the space it explored before the loop closure. It is therefore more suitable for long term operation compared to the conventional CT-SLAM. Furthermore, the proposed method reduces uncertainty in the reconstructed dense map by using probabilistic surface element (surfel) fusion. We demonstrate that the proposed method produces globally consistent maps without global batch trajectory optimization, and effectively reduces LiDAR noise by surfel fusion.

Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach

“…2) Experiments with Real Data: We also performed experiments with real data to evaluate the accuracy of the closeform solution for estimating extrinsic parameters. The mono camera trajectories were estimated by visual odometry [22], while the continuous-time trajectory of the LiDAR sensor were estimated by our previous work [2], [21]. Prior to the closed-form solution calculation, two trajectories are roughly synchronized automatically by detecting the start of the motion using the rotation velocity of the LiDAR trajectory and mean feature movement in the 2D image space as depicted in Fig.…”

Robust Photogeometric Localization Over Time for Map-Centric Loop Closure

“…1. Given a motionundistorted local point cloud from LiDAR and corresponding camera images by the sliding window-based local trajectory optimization [2] as shown Fig. 2, the place recognition module finds a possible revisit of a place and triggers the localization step to estimate an alignment between the current location and the reference location.…”

“…In the proposed method, the synchronization problem of the multi-modal sensors such as different frame-rates or unsynchronized clocks are required to be properly addressed. To cope with this problem, our method proposes to use the continuous-time trajectory representation [2]. In the continuous-time trajectory representation, the trajectory is modeled as a function of time.…”

“…p(u r , D r ) ∈ R 3 is the reconstructed 3D positions of the 2D features. The depth map D r is generated from a dense surfel map by the LiDAR point cloud [2] by rendering at w T L (τ ) L T C where τ follows the reference image time stamp. As the depth is loosely coupled to the image, the 2D geometry of the projected reference patch is not accurately aligned to the source patch in general as shown in the Fig.…”

Skeleton Driven Non-Rigid Motion Tracking and 3D Reconstruction

“…Volumetric 3D reconstruction for rigid scenes and objects is a well studied problem in computer vision and robotics [1,2,3]. Often reconstructed 3D maps are fused with other complementary modalities such as RGB information [4], non-visible imaging information such as thermal-infared [5,6,7] or sound [8] for application such as medical imaging [9], disaster response [10] and energy auditing [11].…”