Evaluation and comparison of eight popular Lidar and Visual SLAM algorithms

In this paper, we evaluate eight popular and open-source 3D Lidar and visual SLAM (Simultaneous Localization and Mapping) algorithms, namely LOAM, Lego LOAM, LIO SAM, HDL Graph, ORB SLAM3, Basalt VIO, and SVO2. We have devised experiments both indoor and outdoor to investigate the effect of the following items: i) effect of mounting positions of the sensors, ii) effect of terrain type and vibration, iii) effect of motion (variation in linear and angular speed). We compare their performance in terms of relative and absolute pose error. We also provide comparison on their required computational resources. We thoroughly analyse and discuss the results and identify best performing system for the environment cases with our multi-camera and multi-Lidar indoor and outdoor datasets. We hope our findings help one to choose a sensor and the corresponding SLAM algorithm combination suiting their needs, based on their target environment.

I. Introduction

Localization and mapping [1] play key roles in various applications, such as, unmanned aerial vehicles [2], unmanned ground vehicles [3], autonomous cars [4], service robots [5], virtual and augmented reality. These technologies are essential components of autonomous robots. They allow a robot to build a map of the environment and to track its relative position within the map. Using the map and the pose information, the robot can perform path planning, navigation, obstacle avoidance, etc.