diff --git a/README.md b/README.md
index 3161722a9..495b80cd5 100644
--- a/README.md
+++ b/README.md
@@ -86,7 +86,7 @@ Our lifelong mapping consists of a few key steps
 - KD-Tree search matching to locate the robot in its position on reinitalization
 - pose-graph optimizition based SLAM with 2D scan matching abstraction 
 
-This will allow the user to create and update existing maps, then serialize the data for use in other mapping sessions, something sorely lacking from most SLAM implementations and nearly all planar SLAM implementations. Other good libraries that do this include RTab-Map and Cartoprapher, though they themselves have their own quirks that make them (in my opinion) unusable for production robotics applications. This library provides the mechanics to save not only the data, but the pose graph, and associated metadata to work with. This has been used to create maps by merging techniques (taking 2 or more serialized objects and creating 1 globally consistent one) as well as continuous mapping techniques (updating 1, same, serialized map object over time and refining it). The major benefit of this over RTab-Map or Cartoprapher is the maturity of the underlying (but heavily modified) `open_karto` library the project is based on. The scan matcher of Karto is well known as an extremely good matcher for 2D laser scans and modified versions of Karto can be found in companies across the world. 
+This will allow the user to create and update existing maps, then serialize the data for use in other mapping sessions, something sorely lacking from most SLAM implementations and nearly all planar SLAM implementations. Other good libraries that do this include RTab-Map and Cartographer, though they themselves have their own quirks that make them (in my opinion) unusable for production robotics applications. This library provides the mechanics to save not only the data, but the pose graph, and associated metadata to work with. This has been used to create maps by merging techniques (taking 2 or more serialized objects and creating 1 globally consistent one) as well as continuous mapping techniques (updating 1, same, serialized map object over time and refining it). The major benefit of this over RTab-Map or Cartographer is the maturity of the underlying (but heavily modified) `open_karto` library the project is based on. The scan matcher of Karto is well known as an extremely good matcher for 2D laser scans and modified versions of Karto can be found in companies across the world. 
 
 Slam Toolbox supports all the major modes:
 - Starting from a predefined dock (assuming to be near start region)