LiDAR

Occasionally, we discover that the LiDAR extrinsic parameters are inaccurate. In such cases, we aim to recalibrate the SLAM poses and maps based on the updated parameters, without the need to rerun the entire SLAM process. By doing so, we can keep the annotations and the original SLAM map, which saves human effort and computational resources. Raw Data Given LiDAR points in the LiDAR coordinate system $$\mathbf{p}^{orig} = \bigcup_{t=0:T}\{\mathbf{p}_{t,i}\}_{i=1}^{N_t}$$Where $t$ is the time index, $i$ is the point index at time $t$, and $N_t$ is the number of points at time $t$. ...

In this post, we will discuss the post-processing of LiDAR SLAM. We mainly focus on its problem formulation. The content of this post follows papers [1] and [2]. Problem Formulation Factor graph representation of bundle adjustment formulation. (Fig. 1 of [2]) With LiDAR poses, each denoted by $\mathbf{T}_j = (\mathbf{R}_j,\mathbf{t}_j)$ $(j=1,\ldots,M_p)$, the bundle adjustment refers to simultaneously determining all the LiDAR poses (denoted by $\mathbf{T} = (\mathbf{T}_1,\cdots,\mathbf{T}_{M_p})$) and feature parameters (denoted by $\boldsymbol{\pi} = (\pi_1,\cdots,\pi_{M_f})$), such that the reconstructed map agrees with the LiDAR measurements to the best extent. Denote $c(\pi_i,\mathbf{T})$ the map consistency due to the $i$-th feature; a straightforward BA formulation is ...

What is SLAM? SLAM demo. SLAM stands for Simultaneous Localization and Mapping. It is a computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it. Applications Object Detection Parking Lot Annotation Lane Annotation Lane Reprojection HD Map [source] SLAM has various applications, including: ...